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Switched to HashBow instead of DBoW3

This commit is contained in:
Vladyslav Usenko 2019-06-07 12:42:18 +00:00
parent a738f46b13
commit 0e073978b9
37 changed files with 220 additions and 6095 deletions
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5
CMakeLists.txt
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@ -193,7 +193,6 @@ include_directories(thirdparty/basalt-headers/thirdparty/cereal/include)
include_directories(thirdparty/basalt-headers/include) include_directories(thirdparty/basalt-headers/include)
include_directories(thirdparty/CLI11/include) include_directories(thirdparty/CLI11/include)
include_directories(thirdparty/fast/include) include_directories(thirdparty/fast/include)
include_directories(thirdparty/DBoW3/src/)
include_directories(include) include_directories(include)
@ -215,7 +214,7 @@ add_library(basalt SHARED
src/utils/keypoints.cpp) src/utils/keypoints.cpp)
target_link_libraries(basalt PUBLIC ${TBB_LIBRARIES} ${STD_CXX_FS} ${OpenCV_LIBS} pangolin PRIVATE rosbag apriltag opengv DBoW3) target_link_libraries(basalt PUBLIC ${TBB_LIBRARIES} ${STD_CXX_FS} ${OpenCV_LIBS} pangolin PRIVATE rosbag apriltag opengv)
add_executable(basalt_calibrate src/calibrate.cpp) add_executable(basalt_calibrate src/calibrate.cpp)
@ -259,7 +258,7 @@ install(TARGETS basalt_calibrate basalt_calibrate_imu basalt_vio_sim basalt_mapp
ARCHIVE DESTINATION ${CMAKE_INSTALL_PREFIX}/lib) ARCHIVE DESTINATION ${CMAKE_INSTALL_PREFIX}/lib)
file(GLOB CONFIG_FILES "${CMAKE_CURRENT_SOURCE_DIR}/data/*.json") file(GLOB CONFIG_FILES "${CMAKE_CURRENT_SOURCE_DIR}/data/*.json")
install(FILES ${CONFIG_FILES} ${CMAKE_CURRENT_SOURCE_DIR}/data/basalt-data/orbvoc.dbow3 install(FILES ${CONFIG_FILES}
DESTINATION ${CMAKE_INSTALL_PREFIX}/etc/basalt) DESTINATION ${CMAKE_INSTALL_PREFIX}/etc/basalt)

3
data/tumvi_512_config.json
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@ -25,6 +25,7 @@
"config.mapper_ransac_threshold": 5e-5, "config.mapper_ransac_threshold": 5e-5,
"config.mapper_min_track_length": 5, "config.mapper_min_track_length": 5,
"config.mapper_max_hamming_distance": 70, "config.mapper_max_hamming_distance": 70,
"config.mapper_second_best_test_ratio": 1.2 "config.mapper_second_best_test_ratio": 1.2,
"config.mapper_bow_num_bits": 16
} }
} }

6
doc/VioMapping.md
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@ -47,9 +47,9 @@ For evaluation the button `align_svd` is used. It aligns the GT trajectory with
### Visual-inertial mapping ### Visual-inertial mapping
To run the mapping tool execute the following command: To run the mapping tool execute the following command:
``` ```
basalt_mapper --cam-calib /usr/etc/basalt/euroc_ds_calib.json --marg-data euroc_marg_data --vocabulary /usr/etc/basalt/orbvoc.dbow3 basalt_mapper --cam-calib /usr/etc/basalt/euroc_ds_calib.json --marg-data euroc_marg_data
``` ```
Here `--marg-data` is the folder with the results from VIO and `--vocabulary` is the path to DBoW3 vocabulary. Here `--marg-data` is the folder with the results from VIO.
This opens the GUI and extracts non-linear factors from the marginalization data. This opens the GUI and extracts non-linear factors from the marginalization data.
![MH_05_MAPPING](/doc/img/MH_05_MAPPING.png) ![MH_05_MAPPING](/doc/img/MH_05_MAPPING.png)
@ -112,6 +112,6 @@ basalt_vio --dataset-path dataset-magistrale1_512_16/ --cam-calib /usr/etc/basal
### Visual-inertial mapping ### Visual-inertial mapping
To run the mapping tool execute the following command: To run the mapping tool execute the following command:
``` ```
basalt_mapper --cam-calib /usr/etc/basalt/tumvi_512_ds_calib.json --marg-data tumvi_marg_data --vocabulary /usr/etc/basalt/orbvoc.dbow3 basalt_mapper --cam-calib /usr/etc/basalt/tumvi_512_ds_calib.json --marg-data tumvi_marg_data
``` ```
![magistrale1_mapping](/doc/img/magistrale1_mapping.png) ![magistrale1_mapping](/doc/img/magistrale1_mapping.png)

159
include/basalt/hash_bow/hash_bow.h Normal file
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@ -0,0 +1,159 @@
#pragma once
#include <array>
#include <bitset>
#include <iostream>
#include <unordered_map>
#include <vector>
#include <basalt/utils/common_types.h>
#include <tbb/concurrent_unordered_map.h>
namespace basalt {
template <size_t N>
class HashBow {
public:
HashBow(size_t num_bits) : num_bits(num_bits < 32 ? num_bits : 32) {
static_assert(N < 512,
"This implementation of HashBow only supports the descriptor "
"length below 512.");
}
inline FeatureHash compute_hash(const std::bitset<N>& descriptor) const {
FeatureHash res;
for (size_t i = 0; i < num_bits; ++i) {
res[i] = descriptor[word_bit_permutation[i]];
}
return res;
}
inline void compute_bow(const std::vector<std::bitset<N>>& descriptors,
std::vector<FeatureHash>& hashes,
HashBowVector& bow_vector) const {
size_t descriptors_size = descriptors.size();
hashes.resize(descriptors_size);
bow_vector.clear();
bow_vector.reserve(descriptors_size);
for (size_t i = 0; i < descriptors_size; i++) {
hashes[i] = compute_hash(descriptors[i]);
FeatureHash bow_word = hashes[i];
bow_vector[bow_word] += 1.0;
}
double sum_squared = 0;
for (const auto& kv : bow_vector) {
sum_squared += kv.second * kv.second;
}
double norm = std::sqrt(sum_squared);
for (auto& kv : bow_vector) {
kv.second /= norm;
}
}
inline void add_to_database(const TimeCamId& tcid,
const HashBowVector& bow_vector) {
for (const auto& kv : bow_vector) {
std::pair<TimeCamId, double> p = std::make_pair(tcid, kv.second);
inverted_index.emplace(kv.first, p);
}
}
inline void querry_database(
const HashBowVector& bow_vector, size_t num_results,
std::vector<std::pair<TimeCamId, double>>& results,
const int64_t* max_t_ns = nullptr) const {
results.clear();
std::unordered_map<TimeCamId, double, tbb::tbb_hash<TimeCamId>> scores;
for (const auto& kv : bow_vector) {
const auto range_it = inverted_index.equal_range(kv.first);
for (auto it = range_it.first; it != range_it.second; ++it) {
// if there is a maximum query time select only the frames that have
// timestamp below max_t_ns
if (!max_t_ns || it->second.first.first < (*max_t_ns))
scores[it->second.first] += kv.second * it->second.second;
}
}
results.reserve(scores.size());
for (const auto& kv : scores) results.emplace_back(kv);
std::sort(results.begin(), results.end(),
[](const auto& a, const auto& b) { return a.second > b.second; });
if (results.size() > num_results) results.resize(num_results);
}
protected:
constexpr static const size_t random_bit_permutation[512] = {
484, 458, 288, 170, 215, 424, 41, 38, 293, 96, 172, 428, 508, 52, 370,
1, 182, 472, 89, 339, 273, 234, 98, 217, 73, 195, 307, 306, 113, 429,
161, 443, 364, 439, 301, 247, 325, 24, 490, 366, 75, 7, 464, 232, 49,
196, 144, 69, 470, 387, 3, 86, 361, 313, 396, 356, 94, 201, 291, 360,
107, 251, 413, 393, 296, 124, 308, 146, 298, 160, 121, 302, 151, 345, 336,
26, 63, 238, 79, 267, 262, 437, 433, 350, 53, 134, 194, 452, 114, 54,
82, 214, 191, 242, 482, 37, 432, 311, 130, 460, 422, 221, 271, 192, 474,
46, 289, 34, 20, 95, 463, 499, 159, 272, 481, 129, 448, 173, 323, 258,
416, 229, 334, 510, 461, 263, 362, 346, 39, 500, 381, 401, 492, 299, 33,
169, 241, 11, 254, 449, 199, 486, 400, 365, 70, 436, 108, 19, 233, 505,
152, 6, 480, 468, 278, 426, 253, 471, 328, 327, 139, 29, 27, 488, 332,
290, 412, 164, 259, 352, 222, 186, 32, 319, 410, 211, 405, 187, 213, 507,
205, 395, 62, 178, 36, 140, 87, 491, 351, 450, 314, 77, 342, 132, 133,
477, 103, 389, 206, 197, 324, 485, 425, 297, 231, 123, 447, 126, 9, 64,
181, 40, 14, 5, 261, 431, 333, 223, 4, 138, 220, 76, 44, 300, 331,
78, 193, 497, 403, 435, 275, 147, 66, 368, 141, 451, 225, 250, 61, 18,
444, 208, 380, 109, 255, 337, 372, 212, 359, 457, 31, 398, 354, 219, 117,
248, 392, 203, 88, 479, 509, 149, 120, 145, 51, 15, 367, 190, 163, 417,
454, 329, 183, 390, 83, 404, 249, 81, 264, 445, 317, 179, 244, 473, 71,
111, 118, 209, 171, 224, 459, 446, 104, 13, 377, 200, 414, 198, 420, 226,
153, 384, 25, 441, 305, 338, 316, 483, 184, 402, 48, 131, 502, 252, 469,
12, 167, 243, 373, 35, 127, 341, 455, 379, 210, 340, 128, 430, 57, 434,
330, 415, 494, 142, 355, 282, 322, 65, 105, 421, 68, 409, 466, 245, 59,
269, 112, 386, 257, 256, 93, 174, 16, 60, 143, 343, 115, 506, 276, 10,
496, 489, 235, 47, 136, 22, 165, 204, 42, 465, 440, 498, 312, 504, 116,
419, 185, 303, 218, 353, 283, 374, 2, 177, 137, 240, 102, 309, 292, 85,
453, 388, 397, 438, 281, 279, 442, 110, 55, 101, 100, 150, 375, 406, 157,
23, 0, 237, 376, 236, 216, 8, 154, 91, 456, 423, 176, 427, 284, 30,
84, 349, 335, 56, 270, 227, 286, 168, 239, 122, 478, 162, 475, 166, 17,
348, 285, 175, 155, 266, 382, 304, 268, 180, 295, 125, 371, 467, 277, 294,
58, 347, 72, 280, 50, 287, 511, 80, 260, 326, 495, 45, 106, 399, 369,
503, 357, 315, 418, 487, 99, 43, 320, 188, 407, 246, 501, 119, 158, 274,
408, 230, 358, 90, 148, 363, 207, 344, 265, 462, 189, 310, 385, 67, 28,
383, 378, 156, 394, 97, 476, 493, 321, 411, 228, 21, 391, 202, 92, 318,
74, 135};
constexpr static std::array<size_t, FEATURE_HASH_MAX_SIZE>
compute_permutation() {
std::array<size_t, FEATURE_HASH_MAX_SIZE> res{};
size_t j = 0;
for (size_t i = 0; i < 512 && j < FEATURE_HASH_MAX_SIZE; ++i) {
if (random_bit_permutation[i] < N) {
res[j] = random_bit_permutation[i];
j++;
}
}
return res;
}
constexpr static const std::array<size_t, FEATURE_HASH_MAX_SIZE>
word_bit_permutation = compute_permutation();
size_t num_bits;
tbb::concurrent_unordered_multimap<FeatureHash, std::pair<TimeCamId, double>,
std::hash<FeatureHash>>
inverted_index;
};
} // namespace basalt

11
include/basalt/utils/common_types.h
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@ -48,6 +48,9 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
namespace basalt { namespace basalt {
/// ids for 2D features detected in images
using FeatureId = int;
/// identifies a frame of multiple images (stereo pair) /// identifies a frame of multiple images (stereo pair)
using FrameId = int64_t; using FrameId = int64_t;
@ -61,8 +64,9 @@ inline std::ostream& operator<<(std::ostream& os, const TimeCamId& tcid) {
return os; return os;
} }
/// ids for 2D features detected in images constexpr static const size_t FEATURE_HASH_MAX_SIZE = 32;
using FeatureId = int; using FeatureHash = std::bitset<FEATURE_HASH_MAX_SIZE>;
using HashBowVector = std::unordered_map<FeatureHash, double>;
/// keypoint positions and descriptors for an image /// keypoint positions and descriptors for an image
struct KeypointsData { struct KeypointsData {
@ -77,6 +81,9 @@ struct KeypointsData {
std::vector<std::bitset<256>> corner_descriptors; std::vector<std::bitset<256>> corner_descriptors;
Eigen::vector<Eigen::Vector4d> corners_3d; Eigen::vector<Eigen::Vector4d> corners_3d;
std::vector<FeatureHash> hashes;
HashBowVector bow_vector;
}; };
/// feature corners is a collection of { imageId => KeypointsData } /// feature corners is a collection of { imageId => KeypointsData }

5
include/basalt/utils/keypoints.h
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@ -65,11 +65,6 @@ void computeAngles(const basalt::Image<const uint16_t>& img_raw,
void computeDescriptors(const basalt::Image<const uint16_t>& img_raw, void computeDescriptors(const basalt::Image<const uint16_t>& img_raw,
KeypointsData& kd); KeypointsData& kd);
void matchFastHelper(const std::vector<std::bitset<256>>& corner_descriptors_1,
const std::vector<std::bitset<256>>& corner_descriptors_2,
std::map<int, int>& matches, int threshold,
double test_dist);
void matchDescriptors(const std::vector<std::bitset<256>>& corner_descriptors_1, void matchDescriptors(const std::vector<std::bitset<256>>& corner_descriptors_1,
const std::vector<std::bitset<256>>& corner_descriptors_2, const std::vector<std::bitset<256>>& corner_descriptors_2,
std::vector<std::pair<int, int>>& matches, int threshold, std::vector<std::pair<int, int>>& matches, int threshold,

1
include/basalt/utils/vio_config.h
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@ -71,5 +71,6 @@ struct VioConfig {
double mapper_min_track_length; double mapper_min_track_length;
double mapper_max_hamming_distance; double mapper_max_hamming_distance;
double mapper_second_best_test_ratio; double mapper_second_best_test_ratio;
int mapper_bow_num_bits;
}; };
} // namespace basalt } // namespace basalt

18
include/basalt/vi_estimator/nfr_mapper.h
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@ -49,12 +49,11 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <tbb/parallel_for.h> #include <tbb/parallel_for.h>
#include <tbb/parallel_reduce.h> #include <tbb/parallel_reduce.h>
#include <mutex>
#include <DBoW3.h>
namespace basalt { namespace basalt {
template <size_t N>
class HashBow;
class NfrMapper : public BundleAdjustmentBase { class NfrMapper : public BundleAdjustmentBase {
public: public:
using Ptr = std::shared_ptr<NfrMapper>; using Ptr = std::shared_ptr<NfrMapper>;
@ -164,8 +163,7 @@ class NfrMapper : public BundleAdjustmentBase {
const Eigen::map<int64_t, PoseStateWithLin>* frame_poses; const Eigen::map<int64_t, PoseStateWithLin>* frame_poses;
}; };
NfrMapper(const basalt::Calibration<double>& calib, const VioConfig& config, NfrMapper(const basalt::Calibration<double>& calib, const VioConfig& config);
const std::string& vocabulary = "");
void addMargData(basalt::MargData::Ptr& data); void addMargData(basalt::MargData::Ptr& data);
@ -199,19 +197,13 @@ class NfrMapper : public BundleAdjustmentBase {
std::unordered_map<int64_t, OpticalFlowInput::Ptr> img_data; std::unordered_map<int64_t, OpticalFlowInput::Ptr> img_data;
tbb::concurrent_unordered_map<
TimeCamId, std::pair<DBoW3::BowVector, DBoW3::FeatureVector>>
bow_data;
Corners feature_corners; Corners feature_corners;
Matches feature_matches; Matches feature_matches;
FeatureTracks feature_tracks; FeatureTracks feature_tracks;
DBoW3::Database bow_database; std::shared_ptr<HashBow<256>> hash_bow_database;
std::unordered_map<int, TimeCamId> bow_id_to_tcid;
VioConfig config; VioConfig config;
}; };

2
scripts/eval_full/run_evaluations.sh
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@ -19,7 +19,7 @@ for d in ${DATASETS[$CI_NODE_INDEX-1]}; do
--result-path $folder_name/vio_$d --marg-data eval_tmp_marg_data --result-path $folder_name/vio_$d --marg-data eval_tmp_marg_data
basalt_mapper --show-gui 0 --cam-calib /usr/etc/basalt/euroc_eucm_calib.json --marg-data eval_tmp_marg_data \ basalt_mapper --show-gui 0 --cam-calib /usr/etc/basalt/euroc_eucm_calib.json --marg-data eval_tmp_marg_data \
--vocabulary /usr/etc/basalt/orbvoc.dbow3 --result-path $folder_name/mapper_$d --result-path $folder_name/mapper_$d
rm -rf eval_tmp_marg_data rm -rf eval_tmp_marg_data
done done

5
src/mapper.cpp
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@ -140,7 +140,6 @@ Button align_btn("ui.aling_svd", &alignButton);
pangolin::OpenGlRenderState camera; pangolin::OpenGlRenderState camera;
std::string marg_data_path; std::string marg_data_path;
std::string vocabulary;
int main(int argc, char** argv) { int main(int argc, char** argv) {
bool show_gui = true; bool show_gui = true;
@ -160,8 +159,6 @@ int main(int argc, char** argv) {
app.add_option("--config-path", config_path, "Path to config file."); app.add_option("--config-path", config_path, "Path to config file.");
app.add_option("--vocabulary", vocabulary, "Path to vocabulary.")->required();
app.add_option("--result-path", result_path, "Path to config file."); app.add_option("--result-path", result_path, "Path to config file.");
try { try {
@ -537,7 +534,7 @@ void load_data(const std::string& calib_path, const std::string& cache_path) {
} }
} }
nrf_mapper.reset(new basalt::NfrMapper(calib, vio_config, vocabulary)); nrf_mapper.reset(new basalt::NfrMapper(calib, vio_config));
basalt::MargDataLoader mdl; basalt::MargDataLoader mdl;
tbb::concurrent_bounded_queue<basalt::MargData::Ptr> marg_queue; tbb::concurrent_bounded_queue<basalt::MargData::Ptr> marg_queue;

10
src/utils/keypoints.cpp
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@ -33,6 +33,8 @@ OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/ */
#include <unordered_set>
#include <basalt/utils/keypoints.h> #include <basalt/utils/keypoints.h>
#include <opencv2/features2d/features2d.hpp> #include <opencv2/features2d/features2d.hpp>
@ -305,7 +307,7 @@ void computeDescriptors(const basalt::Image<const uint16_t>& img_raw,
void matchFastHelper(const std::vector<std::bitset<256>>& corner_descriptors_1, void matchFastHelper(const std::vector<std::bitset<256>>& corner_descriptors_1,
const std::vector<std::bitset<256>>& corner_descriptors_2, const std::vector<std::bitset<256>>& corner_descriptors_2,
std::map<int, int>& matches, int threshold, std::unordered_map<int, int>& matches, int threshold,
double test_dist) { double test_dist) {
matches.clear(); matches.clear();
@ -338,7 +340,7 @@ void matchDescriptors(const std::vector<std::bitset<256>>& corner_descriptors_1,
double dist_2_best) { double dist_2_best) {
matches.clear(); matches.clear();
std::map<int, int> matches_1_2, matches_2_1; std::unordered_map<int, int> matches_1_2, matches_2_1;
matchFastHelper(corner_descriptors_1, corner_descriptors_2, matches_1_2, matchFastHelper(corner_descriptors_1, corner_descriptors_2, matches_1_2,
threshold, dist_2_best); threshold, dist_2_best);
matchFastHelper(corner_descriptors_2, corner_descriptors_1, matches_2_1, matchFastHelper(corner_descriptors_2, corner_descriptors_1, matches_2_1,
@ -397,8 +399,8 @@ void findInliersRansac(const KeypointsData& kd1, const KeypointsData& kd2,
ransac.sac_model_->selectWithinDistance(nonlinear_transformation, ransac.sac_model_->selectWithinDistance(nonlinear_transformation,
ransac.threshold_, ransac.inliers_); ransac.threshold_, ransac.inliers_);
// Sanity check if the number of inliers decreased, but only warn if it is by // Sanity check if the number of inliers decreased, but only warn if it is
// 3 or more, since some small fluctuation is expected. // by 3 or more, since some small fluctuation is expected.
if (ransac.inliers_.size() + 2 < num_inliers_ransac) { if (ransac.inliers_.size() + 2 < num_inliers_ransac) {
std::cout << "Warning: non-linear refinement reduced the relative pose " std::cout << "Warning: non-linear refinement reduced the relative pose "
"ransac inlier count from " "ransac inlier count from "

4
src/utils/vio_config.cpp
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@ -65,12 +65,13 @@ VioConfig::VioConfig() {
mapper_obs_huber_thresh = 1.5; mapper_obs_huber_thresh = 1.5;
mapper_detection_num_points = 800; mapper_detection_num_points = 800;
mapper_num_frames_to_match = 30; mapper_num_frames_to_match = 30;
mapper_frames_to_match_threshold = 0.3; mapper_frames_to_match_threshold = 0.04;
mapper_min_matches = 20; mapper_min_matches = 20;
mapper_ransac_threshold = 5e-5; mapper_ransac_threshold = 5e-5;
mapper_min_track_length = 5; mapper_min_track_length = 5;
mapper_max_hamming_distance = 70; mapper_max_hamming_distance = 70;
mapper_second_best_test_ratio = 1.2; mapper_second_best_test_ratio = 1.2;
mapper_bow_num_bits = 16;
} }
void VioConfig::save(const std::string& filename) { void VioConfig::save(const std::string& filename) {
@ -126,5 +127,6 @@ void serialize(Archive& ar, basalt::VioConfig& config) {
ar(CEREAL_NVP(config.mapper_min_track_length)); ar(CEREAL_NVP(config.mapper_min_track_length));
ar(CEREAL_NVP(config.mapper_max_hamming_distance)); ar(CEREAL_NVP(config.mapper_max_hamming_distance));
ar(CEREAL_NVP(config.mapper_second_best_test_ratio)); ar(CEREAL_NVP(config.mapper_second_best_test_ratio));
ar(CEREAL_NVP(config.mapper_bow_num_bits));
} }
} // namespace cereal } // namespace cereal

79
src/vi_estimator/nfr_mapper.cpp
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@ -39,21 +39,17 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <basalt/utils/tracks.h> #include <basalt/utils/tracks.h>
#include <basalt/vi_estimator/nfr_mapper.h> #include <basalt/vi_estimator/nfr_mapper.h>
#include <DBoW3.h> #include <basalt/hash_bow/hash_bow.h>
namespace basalt { namespace basalt {
NfrMapper::NfrMapper(const Calibration<double>& calib, const VioConfig& config, NfrMapper::NfrMapper(const Calibration<double>& calib, const VioConfig& config)
const std::string& vocabulary)
: config(config) { : config(config) {
this->calib = calib; this->calib = calib;
this->obs_std_dev = config.mapper_obs_std_dev; this->obs_std_dev = config.mapper_obs_std_dev;
this->huber_thresh = config.mapper_obs_huber_thresh; this->huber_thresh = config.mapper_obs_huber_thresh;
if (!vocabulary.empty()) { hash_bow_database.reset(new HashBow<256>(config.mapper_bow_num_bits));
DBoW3::Vocabulary voc(vocabulary);
bow_database.setVocabulary(voc);
}
} }
void NfrMapper::addMargData(MargData::Ptr& data) { void NfrMapper::addMargData(MargData::Ptr& data) {
@ -378,7 +374,7 @@ void NfrMapper::detect_keypoints() {
if (kv->second.get()) { if (kv->second.get()) {
for (size_t i = 0; i < kv->second->img_data.size(); i++) { for (size_t i = 0; i < kv->second->img_data.size(); i++) {
TimeCamId tcid(kv->first, i); TimeCamId tcid(kv->first, i);
KeypointsData kd; KeypointsData& kd = feature_corners[tcid];
if (!kv->second->img_data[i].img.get()) continue; if (!kv->second->img_data[i].img.get()) continue;
@ -394,18 +390,13 @@ void NfrMapper::detect_keypoints() {
calib.intrinsics[tcid.second].unproject(kd.corners, kd.corners_3d, calib.intrinsics[tcid.second].unproject(kd.corners, kd.corners_3d,
success); success);
feature_corners[tcid] = kd; hash_bow_database->compute_bow(kd.corner_descriptors, kd.hashes,
kd.bow_vector);
auto& bow = bow_data[tcid]; hash_bow_database->add_to_database(tcid, kd.bow_vector);
if (bow_database.usingDirectIndex()) { // std::cout << "bow " << kd.bow_vector.size() << " desc "
bow_database.getVocabulary()->transform( // << kd.corner_descriptors.size() << std::endl;
kd.corner_descriptors, bow.first, bow.second,
bow_database.getDirectIndexLevels());
} else {
bow_database.getVocabulary()->transform(kd.corner_descriptors,
bow.first);
}
} }
} }
} }
@ -413,28 +404,13 @@ void NfrMapper::detect_keypoints() {
auto t2 = std::chrono::high_resolution_clock::now(); auto t2 = std::chrono::high_resolution_clock::now();
for (const auto& kv : bow_data) {
int bow_id;
if (bow_database.usingDirectIndex()) {
bow_id = bow_database.add(kv.second.first, kv.second.second);
} else {
bow_id = bow_database.add(kv.second.first);
}
bow_id_to_tcid[bow_id] = kv.first;
}
auto t3 = std::chrono::high_resolution_clock::now();
auto elapsed1 = auto elapsed1 =
std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1); std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1);
auto elapsed2 =
std::chrono::duration_cast<std::chrono::microseconds>(t3 - t2);
std::cout << "Processed " << feature_corners.size() << " frames." std::cout << "Processed " << feature_corners.size() << " frames."
<< std::endl; << std::endl;
std::cout << "Detection time: " << elapsed1.count() * 1e-6 std::cout << "Detection time: " << elapsed1.count() * 1e-6 << "s."
<< "s. Adding to DB time: " << elapsed2.count() * 1e-6 << "s."
<< std::endl; << std::endl;
} }
@ -502,33 +478,34 @@ void NfrMapper::match_all() {
tbb::blocked_range<size_t> keys_range(0, keys.size()); tbb::blocked_range<size_t> keys_range(0, keys.size());
auto compute_pairs = [&](const tbb::blocked_range<size_t>& r) { auto compute_pairs = [&](const tbb::blocked_range<size_t>& r) {
for (size_t i = r.begin(); i != r.end(); ++i) { for (size_t i = r.begin(); i != r.end(); ++i) {
DBoW3::QueryResults q; const TimeCamId& tcid = keys[i];
const KeypointsData& kd = feature_corners.at(tcid);
auto it = bow_data.find(keys[i]); std::vector<std::pair<TimeCamId, double>> results;
if (it != bow_data.end()) { hash_bow_database->querry_database(kd.bow_vector,
bow_database.query(it->second.first, q, config.mapper_num_frames_to_match,
config.mapper_num_frames_to_match); results, &tcid.first);
for (const auto& r : q) { // std::cout << "Closest frames for " << tcid << ": ";
// Match only previous frames for (const auto& otcid_score : results) {
// std::cout << otcid_score.first << "(" << otcid_score.second << ") ";
if (otcid_score.first.first != tcid.first &&
otcid_score.second > config.mapper_frames_to_match_threshold) {
match_pair m;
m.i = i;
m.j = id_to_key_idx.at(otcid_score.first);
m.score = otcid_score.second;
size_t j = id_to_key_idx.at(bow_id_to_tcid.at(r.Id)); ids_to_match.emplace_back(m);
if (r.Score > config.mapper_frames_to_match_threshold &&
keys[i].first < keys[j].first) {
match_pair m;
m.i = i;
m.j = j;
m.score = r.Score;
ids_to_match.emplace_back(m);
}
} }
} }
// std::cout << std::endl;
} }
}; };
tbb::parallel_for(keys_range, compute_pairs); tbb::parallel_for(keys_range, compute_pairs);
// compute_pairs(keys_range);
auto t2 = std::chrono::high_resolution_clock::now(); auto t2 = std::chrono::high_resolution_clock::now();

1
thirdparty/CMakeLists.txt vendored
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@ -2,7 +2,6 @@ cmake_minimum_required(VERSION 3.10)
add_subdirectory(ros EXCLUDE_FROM_ALL) add_subdirectory(ros EXCLUDE_FROM_ALL)
add_subdirectory(apriltag EXCLUDE_FROM_ALL) add_subdirectory(apriltag EXCLUDE_FROM_ALL)
add_subdirectory(DBoW3 EXCLUDE_FROM_ALL)
set(BUILD_SHARED_LIBS OFF CACHE BOOL "Enable BUILD_SHARED_LIBS") set(BUILD_SHARED_LIBS OFF CACHE BOOL "Enable BUILD_SHARED_LIBS")

15
thirdparty/DBoW3/CMakeLists.txt vendored
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@ -1,15 +0,0 @@
cmake_minimum_required(VERSION 2.8)
PROJECT(DBoW3)
set(PROJECT_VERSION "0.0.1")
string(REGEX MATCHALL "[0-9]" PROJECT_VERSION_PARTS "${PROJECT_VERSION}")
list(GET PROJECT_VERSION_PARTS 0 PROJECT_VERSION_MAJOR)
list(GET PROJECT_VERSION_PARTS 1 PROJECT_VERSION_MINOR)
list(GET PROJECT_VERSION_PARTS 2 PROJECT_VERSION_PATCH)
set(PROJECT_SOVERSION "${PROJECT_VERSION_MAJOR}.${PROJECT_VERSION_MINOR}")
#------------------------------------------------
# DIRS
#------------------------------------------------
ADD_SUBDIRECTORY(src)
set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/lib)

44
thirdparty/DBoW3/LICENSE.txt vendored
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@ -1,44 +0,0 @@
DBoW3: bag-of-words library for C++ with generic descriptors
Copyright (c) 2015 Dorian Galvez-Lopez <http://doriangalvez.com> (Universidad de Zaragoza)
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither the name of copyright holders nor the names of its
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
''AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDERS OR CONTRIBUTORS
BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
If you use it in an academic work, please cite:
@ARTICLE{GalvezTRO12,
author={G\'alvez-L\'opez, Dorian and Tard\'os, J. D.},
journal={IEEE Transactions on Robotics},
title={Bags of Binary Words for Fast Place Recognition in Image Sequences},
year={2012},
month={October},
volume={28},
number={5},
pages={1188--1197},
doi={10.1109/TRO.2012.2197158},
ISSN={1552-3098}
}

7
thirdparty/DBoW3/README.txt vendored
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@ -1,7 +0,0 @@
You should have received this DBoW3 version along with ORB-SLAM2 (https://github.com/raulmur/ORB_SLAM2).
See the original DBoW3 library at: https://github.com/dorian3d/DBoW3
All files included in this version are BSD, see LICENSE.txt
We also use Random.h, Random.cpp, Timestamp.pp and Timestamp.h from DLib/DUtils.
See the original DLib library at: https://github.com/dorian3d/DLib
All files included in this version are BSD, see LICENSE.txt

136
thirdparty/DBoW3/src/BowVector.cpp vendored
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@ -1,136 +0,0 @@
/**
* File: BowVector.cpp
* Date: March 2011
* Author: Dorian Galvez-Lopez
* Description: bag of words vector
* License: see the LICENSE.txt file
*
*/
#include <algorithm>
#include <cmath>
#include <fstream>
#include <iostream>
#include <vector>
#include "BowVector.h"
namespace DBoW3 {
// --------------------------------------------------------------------------
BowVector::BowVector(void) {}
// --------------------------------------------------------------------------
BowVector::~BowVector(void) {}
// --------------------------------------------------------------------------
void BowVector::addWeight(WordId id, WordValue v) {
BowVector::iterator vit = this->lower_bound(id);
if (vit != this->end() && !(this->key_comp()(id, vit->first))) {
vit->second += v;
} else {
this->insert(vit, BowVector::value_type(id, v));
}
}
// --------------------------------------------------------------------------
void BowVector::addIfNotExist(WordId id, WordValue v) {
BowVector::iterator vit = this->lower_bound(id);
if (vit == this->end() || (this->key_comp()(id, vit->first))) {
this->insert(vit, BowVector::value_type(id, v));
}
}
// --------------------------------------------------------------------------
void BowVector::normalize(LNorm norm_type) {
double norm = 0.0;
BowVector::iterator it;
if (norm_type == DBoW3::L1) {
for (it = begin(); it != end(); ++it) norm += fabs(it->second);
} else {
for (it = begin(); it != end(); ++it) norm += it->second * it->second;
norm = sqrt(norm);
}
if (norm > 0.0) {
for (it = begin(); it != end(); ++it) it->second /= norm;
}
}
// --------------------------------------------------------------------------
std::ostream &operator<<(std::ostream &out, const BowVector &v) {
BowVector::const_iterator vit;
// std::vector<unsigned int>::const_iterator iit;
unsigned int i = 0;
const size_t N = v.size();
for (vit = v.begin(); vit != v.end(); ++vit, ++i) {
out << "<" << vit->first << ", " << vit->second << ">";
if (i < N - 1) out << ", ";
}
return out;
}
// --------------------------------------------------------------------------
void BowVector::saveM(const std::string &filename, size_t W) const {
std::fstream f(filename.c_str(), std::ios::out);
WordId last = 0;
BowVector::const_iterator bit;
for (bit = this->begin(); bit != this->end(); ++bit) {
for (; last < bit->first; ++last) {
f << "0 ";
}
f << bit->second << " ";
last = bit->first + 1;
}
for (; last < (WordId)W; ++last) f << "0 ";
f.close();
}
// --------------------------------------------------------------------------
void BowVector::toStream(std::ostream &str) const {
uint32_t s = size();
str.write((char *)&s, sizeof(s));
for (auto d : *this) {
str.write((char *)&d.first, sizeof(d.first));
str.write((char *)&d.second, sizeof(d.second));
}
}
// --------------------------------------------------------------------------
void BowVector::fromStream(std::istream &str) {
clear();
uint32_t s;
str.read((char *)&s, sizeof(s));
for (uint32_t i = 0; i < s; i++) {
WordId wid;
WordValue wv;
str.read((char *)&wid, sizeof(wid));
str.read((char *)&wv, sizeof(wv));
insert(std::make_pair(wid, wv));
}
}
uint64_t BowVector::getSignature() const {
uint64_t sig = 0;
for (auto ww : *this) sig += ww.first + 1e6 * ww.second;
return sig;
}
// --------------------------------------------------------------------------
} // namespace DBoW3

117
thirdparty/DBoW3/src/BowVector.h vendored
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@ -1,117 +0,0 @@
/**
* File: BowVector.h
* Date: March 2011
* Author: Dorian Galvez-Lopez
* Description: bag of words vector
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_BOW_VECTOR__
#define __D_T_BOW_VECTOR__
#include <map>
#include <vector>
#include "exports.h"
#if _WIN32
#include <cstdint>
#endif
namespace DBoW3 {
/// Id of words
typedef unsigned int WordId;
/// Value of a word
typedef double WordValue;
/// Id of nodes in the vocabulary tree
typedef unsigned int NodeId;
/// L-norms for normalization
enum LNorm
{
L1,
L2
};
/// Weighting type
enum WeightingType
{
TF_IDF,
TF,
IDF,
BINARY
};
/// Scoring type
enum ScoringType
{
L1_NORM,
L2_NORM,
CHI_SQUARE,
KL,
BHATTACHARYYA,
DOT_PRODUCT
};
/// Vector of words to represent images
class DBOW_API BowVector:
public std::map<WordId, WordValue>
{
public:
/**
* Constructor
*/
BowVector(void);
/**
* Destructor
*/
~BowVector(void);
/**
* Adds a value to a word value existing in the vector, or creates a new
* word with the given value
* @param id word id to look for
* @param v value to create the word with, or to add to existing word
*/
void addWeight(WordId id, WordValue v);
/**
* Adds a word with a value to the vector only if this does not exist yet
* @param id word id to look for
* @param v value to give to the word if this does not exist
*/
void addIfNotExist(WordId id, WordValue v);
/**
* L1-Normalizes the values in the vector
* @param norm_type norm used
*/
void normalize(LNorm norm_type);
/**
* Prints the content of the bow vector
* @param out stream
* @param v
*/
friend std::ostream& operator<<(std::ostream &out, const BowVector &v);
/**
* Saves the bow vector as a vector in a matlab file
* @param filename
* @param W number of words in the vocabulary
*/
void saveM(const std::string &filename, size_t W) const;
//returns a unique number from the configuration
uint64_t getSignature()const;
//serialization
void toStream(std::ostream &str)const;
void fromStream(std::istream &str);
};
} // namespace DBoW3
#endif

20
thirdparty/DBoW3/src/CMakeLists.txt vendored
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@ -1,20 +0,0 @@
FILE(GLOB hdrs_base "*.h" )
FILE(GLOB srcs_base "*.c*")
FILE(GLOB hdrs ${hdrs_base} )
FILE(GLOB srcs ${srcs_base} )
ADD_LIBRARY(${PROJECT_NAME} ${srcs} ${hdrs})
INCLUDE_DIRECTORIES(${CMAKE_CURRENT_BINARY_DIR} )
SET_TARGET_PROPERTIES(${PROJECT_NAME} PROPERTIES # create *nix style library versions + symbolic links
DEFINE_SYMBOL DBOW_DSO_EXPORTS
VERSION ${PROJECT_VERSION}
SOVERSION ${PROJECT_SOVERSION}
CLEAN_DIRECT_OUTPUT 1 # allow creating static and shared libs without conflicts
OUTPUT_NAME "${PROJECT_NAME}${PROJECT_DLLVERSION}" # avoid conflicts between library and binary target names
)
TARGET_LINK_LIBRARIES(${PROJECT_NAME} ${REQUIRED_LIBRARIES} ${OpenCV_LIBS})

68
thirdparty/DBoW3/src/DBoW3.h vendored
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@ -1,68 +0,0 @@
/*
* File: DBoW3.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: Generic include file for the DBoW3 classes and
* the specialized vocabularies and databases
* License: see the LICENSE.txt file
*
*/
/*! \mainpage DBoW3 Library
*
* DBoW3 library for C++:
* Bag-of-word image database for image retrieval.
*
* Written by Rafael Muñoz Salinas,
* University of Cordoba (Spain)
*
*
* \section requirements Requirements
* This library requires the OpenCV libraries,
* as well as the boost::dynamic_bitset class.
*
* \section citation Citation
* If you use this software in academic works, please cite:
<pre>
@@ARTICLE{GalvezTRO12,
author={Galvez-Lopez, Dorian and Tardos, J. D.},
journal={IEEE Transactions on Robotics},
title={Bags of Binary Words for Fast Place Recognition in Image Sequences},
year={2012},
month={October},
volume={28},
number={5},
pages={1188--1197},
doi={10.1109/TRO.2012.2197158},
ISSN={1552-3098}
}
</pre>
*
* \section license License
* This file is licensed under a Creative Commons
* Attribution-NonCommercial-ShareAlike 3.0 license.
* This file can be freely used and users can use, download and edit this file
* provided that credit is attributed to the original author. No users are
* permitted to use this file for commercial purposes unless explicit permission
* is given by the original author. Derivative works must be licensed using the
* same or similar license.
* Check http://creativecommons.org/licenses/by-nc-sa/3.0/ to obtain further
* details.
*
*/
#ifndef __D_T_DBOW3__
#define __D_T_DBOW3__
/// Includes all the data structures to manage vocabularies and image databases
#include "Vocabulary.h"
#include "Database.h"
#include "BowVector.h"
#include "FeatureVector.h"
#include "QueryResults.h"
#endif

855
thirdparty/DBoW3/src/Database.cpp vendored
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@ -1,855 +0,0 @@
#include "Database.h"
#include <unordered_map>
namespace DBoW3 {
// For query functions
static int MIN_COMMON_WORDS = 5;
// --------------------------------------------------------------------------
Database::Database(bool use_di, int di_levels)
: m_voc(NULL), m_use_di(use_di), m_dilevels(di_levels), m_nentries(0) {}
// --------------------------------------------------------------------------
Database::Database(const Vocabulary &voc, bool use_di, int di_levels)
: m_voc(NULL), m_use_di(use_di), m_dilevels(di_levels) {
setVocabulary(voc);
clear();
}
// --------------------------------------------------------------------------
Database::Database(const Database &db) : m_voc(NULL) { *this = db; }
// --------------------------------------------------------------------------
Database::Database(const std::string &filename) : m_voc(NULL) {
load(filename);
}
// --------------------------------------------------------------------------
Database::Database(const char *filename) : m_voc(NULL) { load(filename); }
// --------------------------------------------------------------------------
Database::~Database(void) { delete m_voc; }
// --------------------------------------------------------------------------
Database &Database::operator=(const Database &db) {
if (this != &db) {
m_dfile = db.m_dfile;
m_dilevels = db.m_dilevels;
m_ifile = db.m_ifile;
m_nentries = db.m_nentries;
m_use_di = db.m_use_di;
if (db.m_voc != 0) setVocabulary(*db.m_voc);
}
return *this;
}
// --------------------------------------------------------------------------
EntryId Database::add(const cv::Mat &features, BowVector *bowvec,
FeatureVector *fvec) {
std::vector<cv::Mat> vf(features.rows);
for (int r = 0; r < features.rows; r++) vf[r] = features.rowRange(r, r + 1);
return add(vf, bowvec, fvec);
}
EntryId Database::add(const std::vector<cv::Mat> &features, BowVector *bowvec,
FeatureVector *fvec) {
BowVector aux;
BowVector &v = (bowvec ? *bowvec : aux);
if (m_use_di && fvec != NULL) {
m_voc->transform(features, v, *fvec, m_dilevels); // with features
return add(v, *fvec);
} else if (m_use_di) {
FeatureVector fv;
m_voc->transform(features, v, fv, m_dilevels); // with features
return add(v, fv);
} else if (fvec != NULL) {
m_voc->transform(features, v, *fvec, m_dilevels); // with features
return add(v);
} else {
m_voc->transform(features, v); // with features
return add(v);
}
}
EntryId Database::add(const std::vector<std::bitset<256>> &features,
BowVector *bowvec, FeatureVector *fvec) {
BowVector aux;
BowVector &v = (bowvec ? *bowvec : aux);
if (m_use_di && fvec != NULL) {
m_voc->transform(features, v, *fvec, m_dilevels); // with features
return add(v, *fvec);
} else if (m_use_di) {
FeatureVector fv;
m_voc->transform(features, v, fv, m_dilevels); // with features
return add(v, fv);
} else if (fvec != NULL) {
m_voc->transform(features, v, *fvec, m_dilevels); // with features
return add(v);
} else {
m_voc->transform(features, v); // with features
return add(v);
}
}
// ---------------------------------------------------------------------------
EntryId Database::add(const BowVector &v, const FeatureVector &fv) {
EntryId entry_id = m_nentries++;
BowVector::const_iterator vit;
// std::vector<unsigned int>::const_iterator iit;
if (m_use_di) {
// update direct file
if (entry_id == m_dfile.size()) {
m_dfile.push_back(fv);
} else {
m_dfile[entry_id] = fv;
}
}
// update inverted file
for (vit = v.begin(); vit != v.end(); ++vit) {
const WordId &word_id = vit->first;
const WordValue &word_weight = vit->second;
IFRow &ifrow = m_ifile[word_id];
ifrow.push_back(IFPair(entry_id, word_weight));
}
return entry_id;
}
// --------------------------------------------------------------------------
void Database::setVocabulary(const Vocabulary &voc) {
delete m_voc;
m_voc = new Vocabulary(voc);
clear();
}
// --------------------------------------------------------------------------
void Database::setVocabulary(const Vocabulary &voc, bool use_di,
int di_levels) {
m_use_di = use_di;
m_dilevels = di_levels;
delete m_voc;
m_voc = new Vocabulary(voc);
clear();
}
// --------------------------------------------------------------------------
void Database::clear() {
// resize vectors
m_ifile.resize(0);
m_ifile.resize(m_voc->size());
m_dfile.resize(0);
m_nentries = 0;
}
// --------------------------------------------------------------------------
void Database::allocate(int nd, int ni) {
// m_ifile already contains |words| items
if (ni > 0) {
for (auto rit = m_ifile.begin(); rit != m_ifile.end(); ++rit) {
int n = (int)rit->size();
if (ni > n) {
rit->resize(ni);
rit->resize(n);
}
}
}
if (m_use_di && (int)m_dfile.size() < nd) {
m_dfile.resize(nd);
}
}
// --------------------------------------------------------------------------
void Database::query(const cv::Mat &features, QueryResults &ret,
int max_results, int max_id) const {
std::vector<cv::Mat> vf(features.rows);
for (int r = 0; r < features.rows; r++) vf[r] = features.rowRange(r, r + 1);
query(vf, ret, max_results, max_id);
}
void Database::query(const std::vector<cv::Mat> &features, QueryResults &ret,
int max_results, int max_id) const {
BowVector vec;
m_voc->transform(features, vec);
query(vec, ret, max_results, max_id);
}
void Database::query(const std::vector<std::bitset<256>> &features,
QueryResults &ret, int max_results, int max_id) const {
BowVector vec;
m_voc->transform(features, vec);
query(vec, ret, max_results, max_id);
}
// --------------------------------------------------------------------------
void Database::query(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const {
ret.resize(0);
switch (m_voc->getScoringType()) {
case L1_NORM:
queryL1(vec, ret, max_results, max_id);
break;
case L2_NORM:
queryL2(vec, ret, max_results, max_id);
break;
case CHI_SQUARE:
queryChiSquare(vec, ret, max_results, max_id);
break;
case KL:
queryKL(vec, ret, max_results, max_id);
break;
case BHATTACHARYYA:
queryBhattacharyya(vec, ret, max_results, max_id);
break;
case DOT_PRODUCT:
queryDotProduct(vec, ret, max_results, max_id);
break;
}
}
// --------------------------------------------------------------------------
void Database::queryL1(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const {
BowVector::const_iterator vit;
std::unordered_map<EntryId, double> pairs;
for (vit = vec.begin(); vit != vec.end(); ++vit) {
const WordId word_id = vit->first;
const WordValue &qvalue = vit->second;
const IFRow &row = m_ifile[word_id];
// IFRows are sorted in ascending entry_id order
for (const auto &rit : row) {
const EntryId &entry_id = rit.entry_id;
const WordValue &dvalue = rit.word_weight;
if ((int)entry_id < max_id || max_id == -1) {
double value = fabs(qvalue - dvalue) - fabs(qvalue) - fabs(dvalue);
auto it = pairs.find(entry_id);
if (it != pairs.end()) {
it->second += value;
} else {
pairs.emplace(entry_id, value);
}
}
} // for each inverted row
} // for each query word
// move to vector
ret.reserve(pairs.size());
for (const auto &pit : pairs) {
ret.push_back(Result(pit.first, pit.second));
}
// resulting "scores" are now in [-2 best .. 0 worst]
// sort vector in ascending order of score
std::sort(ret.begin(), ret.end());
// (ret is inverted now --the lower the better--)
// cut vector
if (max_results > 0 && (int)ret.size() > max_results) ret.resize(max_results);
// complete and scale score to [0 worst .. 1 best]
// ||v - w||_{L1} = 2 + Sum(|v_i - w_i| - |v_i| - |w_i|)
// for all i | v_i != 0 and w_i != 0
// (Nister, 2006)
// scaled_||v - w||_{L1} = 1 - 0.5 * ||v - w||_{L1}
QueryResults::iterator qit;
for (qit = ret.begin(); qit != ret.end(); qit++)
qit->Score = -qit->Score / 2.0;
}
// --------------------------------------------------------------------------
void Database::queryL2(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const {
BowVector::const_iterator vit;
std::map<EntryId, double> pairs;
std::map<EntryId, double>::iterator pit;
// map<EntryId, int> counters;
// map<EntryId, int>::iterator cit;
for (vit = vec.begin(); vit != vec.end(); ++vit) {
const WordId word_id = vit->first;
const WordValue &qvalue = vit->second;
const IFRow &row = m_ifile[word_id];
// IFRows are sorted in ascending entry_id order
for (auto rit = row.begin(); rit != row.end(); ++rit) {
const EntryId entry_id = rit->entry_id;
const WordValue &dvalue = rit->word_weight;
if ((int)entry_id < max_id || max_id == -1) {
double value = -qvalue * dvalue; // minus sign for sorting trick
pit = pairs.lower_bound(entry_id);
// cit = counters.lower_bound(entry_id);
if (pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first))) {
pit->second += value;
// cit->second += 1;
} else {
pairs.insert(pit,
std::map<EntryId, double>::value_type(entry_id, value));
// counters.insert(cit,
// map<EntryId, int>::value_type(entry_id, 1));
}
}
} // for each inverted row
} // for each query word
// move to vector
ret.reserve(pairs.size());
// cit = counters.begin();
for (pit = pairs.begin(); pit != pairs.end(); ++pit) //, ++cit)
{
ret.push_back(Result(pit->first, pit->second)); // / cit->second));
}
// resulting "scores" are now in [-1 best .. 0 worst]
// sort vector in ascending order of score
std::sort(ret.begin(), ret.end());
// (ret is inverted now --the lower the better--)
// cut vector
if (max_results > 0 && (int)ret.size() > max_results) ret.resize(max_results);
// complete and scale score to [0 worst .. 1 best]
// ||v - w||_{L2} = sqrt( 2 - 2 * Sum(v_i * w_i)
// for all i | v_i != 0 and w_i != 0 )
// (Nister, 2006)
QueryResults::iterator qit;
for (qit = ret.begin(); qit != ret.end(); qit++) {
if (qit->Score <= -1.0) // rounding error
qit->Score = 1.0;
else
qit->Score = 1.0 - sqrt(1.0 + qit->Score); // [0..1]
// the + sign is ok, it is due to - sign in
// value = - qvalue * dvalue
}
}
// --------------------------------------------------------------------------
void Database::queryChiSquare(const BowVector &vec, QueryResults &ret,
int max_results, int max_id) const {
BowVector::const_iterator vit;
std::map<EntryId, std::pair<double, int>> pairs;
std::map<EntryId, std::pair<double, int>>::iterator pit;
std::map<EntryId, std::pair<double, double>> sums; // < sum vi, sum wi >
std::map<EntryId, std::pair<double, double>>::iterator sit;
// In the current implementation, we suppose vec is not normalized
// map<EntryId, double> expected;
// map<EntryId, double>::iterator eit;
for (vit = vec.begin(); vit != vec.end(); ++vit) {
const WordId word_id = vit->first;
const WordValue &qvalue = vit->second;
const IFRow &row = m_ifile[word_id];
// IFRows are sorted in ascending entry_id order
for (auto rit = row.begin(); rit != row.end(); ++rit) {
const EntryId entry_id = rit->entry_id;
const WordValue &dvalue = rit->word_weight;
if ((int)entry_id < max_id || max_id == -1) {
// (v-w)^2/(v+w) - v - w = -4 vw/(v+w)
// we move the 4 out
double value = 0;
if (qvalue + dvalue != 0.0) // words may have weight zero
value = -qvalue * dvalue / (qvalue + dvalue);
pit = pairs.lower_bound(entry_id);
sit = sums.lower_bound(entry_id);
// eit = expected.lower_bound(entry_id);
if (pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first))) {
pit->second.first += value;
pit->second.second += 1;
// eit->second += dvalue;
sit->second.first += qvalue;
sit->second.second += dvalue;
} else {
pairs.insert(pit,
std::map<EntryId, std::pair<double, int>>::value_type(
entry_id, std::make_pair(value, 1)));
// expected.insert(eit,
// map<EntryId, double>::value_type(entry_id, dvalue));
sums.insert(sit,
std::map<EntryId, std::pair<double, double>>::value_type(
entry_id, std::make_pair(qvalue, dvalue)));
}
}
} // for each inverted row
} // for each query word
// move to vector
ret.reserve(pairs.size());
sit = sums.begin();
for (pit = pairs.begin(); pit != pairs.end(); ++pit, ++sit) {
if (pit->second.second >= MIN_COMMON_WORDS) {
ret.push_back(Result(pit->first, pit->second.first));
ret.back().nWords = pit->second.second;
ret.back().sumCommonVi = sit->second.first;
ret.back().sumCommonWi = sit->second.second;
ret.back().expectedChiScore =
2 * sit->second.second / (1 + sit->second.second);
}
// ret.push_back(Result(pit->first, pit->second));
}
// resulting "scores" are now in [-2 best .. 0 worst]
// we have to add +2 to the scores to obtain the chi square score
// sort vector in ascending order of score
std::sort(ret.begin(), ret.end());
// (ret is inverted now --the lower the better--)
// cut vector
if (max_results > 0 && (int)ret.size() > max_results) ret.resize(max_results);
// complete and scale score to [0 worst .. 1 best]
QueryResults::iterator qit;
for (qit = ret.begin(); qit != ret.end(); qit++) {
// this takes the 4 into account
qit->Score = -2. * qit->Score; // [0..1]
qit->chiScore = qit->Score;
}
}
// --------------------------------------------------------------------------
void Database::queryKL(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const {
BowVector::const_iterator vit;
std::map<EntryId, double> pairs;
std::map<EntryId, double>::iterator pit;
for (vit = vec.begin(); vit != vec.end(); ++vit) {
const WordId word_id = vit->first;
const WordValue &vi = vit->second;
const IFRow &row = m_ifile[word_id];
// IFRows are sorted in ascending entry_id order
for (auto rit = row.begin(); rit != row.end(); ++rit) {
const EntryId entry_id = rit->entry_id;
const WordValue &wi = rit->word_weight;
if ((int)entry_id < max_id || max_id == -1) {
double value = 0;
if (vi != 0 && wi != 0) value = vi * log(vi / wi);
pit = pairs.lower_bound(entry_id);
if (pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first))) {
pit->second += value;
} else {
pairs.insert(pit,
std::map<EntryId, double>::value_type(entry_id, value));
}
}
} // for each inverted row
} // for each query word
// resulting "scores" are now in [-X worst .. 0 best .. X worst]
// but we cannot make sure which ones are better without calculating
// the complete score
// complete scores and move to vector
ret.reserve(pairs.size());
for (pit = pairs.begin(); pit != pairs.end(); ++pit) {
EntryId eid = pit->first;
double value = 0.0;
for (vit = vec.begin(); vit != vec.end(); ++vit) {
const WordValue &vi = vit->second;
const IFRow &row = m_ifile[vit->first];
if (vi != 0) {
if (row.end() == find(row.begin(), row.end(), eid)) {
value += vi * (log(vi) - GeneralScoring::LOG_EPS);
}
}
}
pit->second += value;
// to vector
ret.push_back(Result(pit->first, pit->second));
}
// real scores are now in [0 best .. X worst]
// sort vector in ascending order
// (scores are inverted now --the lower the better--)
std::sort(ret.begin(), ret.end());
// cut vector
if (max_results > 0 && (int)ret.size() > max_results) ret.resize(max_results);
// cannot scale scores
}
// --------------------------------------------------------------------------
void Database::queryBhattacharyya(const BowVector &vec, QueryResults &ret,
int max_results, int max_id) const {
BowVector::const_iterator vit;
// map<EntryId, double> pairs;
// map<EntryId, double>::iterator pit;
std::map<EntryId, std::pair<double, int>> pairs; // <eid, <score, counter> >
std::map<EntryId, std::pair<double, int>>::iterator pit;
for (vit = vec.begin(); vit != vec.end(); ++vit) {
const WordId word_id = vit->first;
const WordValue &qvalue = vit->second;
const IFRow &row = m_ifile[word_id];
// IFRows are sorted in ascending entry_id order
for (auto rit = row.begin(); rit != row.end(); ++rit) {
const EntryId entry_id = rit->entry_id;
const WordValue &dvalue = rit->word_weight;
if ((int)entry_id < max_id || max_id == -1) {
double value = sqrt(qvalue * dvalue);
pit = pairs.lower_bound(entry_id);
if (pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first))) {
pit->second.first += value;
pit->second.second += 1;
} else {
pairs.insert(pit,
std::map<EntryId, std::pair<double, int>>::value_type(
entry_id, std::make_pair(value, 1)));
}
}
} // for each inverted row
} // for each query word
// move to vector
ret.reserve(pairs.size());
for (pit = pairs.begin(); pit != pairs.end(); ++pit) {
if (pit->second.second >= MIN_COMMON_WORDS) {
ret.push_back(Result(pit->first, pit->second.first));
ret.back().nWords = pit->second.second;
ret.back().bhatScore = pit->second.first;
}
}
// scores are already in [0..1]
// sort vector in descending order
std::sort(ret.begin(), ret.end(), Result::gt);
// cut vector
if (max_results > 0 && (int)ret.size() > max_results) ret.resize(max_results);
}
// ---------------------------------------------------------------------------
void Database::queryDotProduct(const BowVector &vec, QueryResults &ret,
int max_results, int max_id) const {
BowVector::const_iterator vit;
std::map<EntryId, double> pairs;
std::map<EntryId, double>::iterator pit;
for (vit = vec.begin(); vit != vec.end(); ++vit) {
const WordId word_id = vit->first;
const WordValue &qvalue = vit->second;
const IFRow &row = m_ifile[word_id];
// IFRows are sorted in ascending entry_id order
for (auto rit = row.begin(); rit != row.end(); ++rit) {
const EntryId entry_id = rit->entry_id;
const WordValue &dvalue = rit->word_weight;
if ((int)entry_id < max_id || max_id == -1) {
double value;
if (this->m_voc->getWeightingType() == BINARY)
value = 1;
else
value = qvalue * dvalue;
pit = pairs.lower_bound(entry_id);
if (pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first))) {
pit->second += value;
} else {
pairs.insert(pit,
std::map<EntryId, double>::value_type(entry_id, value));
}
}
} // for each inverted row
} // for each query word
// move to vector
ret.reserve(pairs.size());
for (pit = pairs.begin(); pit != pairs.end(); ++pit) {
ret.push_back(Result(pit->first, pit->second));
}
// scores are the greater the better
// sort vector in descending order
std::sort(ret.begin(), ret.end(), Result::gt);
// cut vector
if (max_results > 0 && (int)ret.size() > max_results) ret.resize(max_results);
// these scores cannot be scaled
}
// ---------------------------------------------------------------------------
const FeatureVector &Database::retrieveFeatures(EntryId id) const {
assert(id < size());
return m_dfile[id];
}
// --------------------------------------------------------------------------
void Database::save(const std::string &filename) const {
cv::FileStorage fs(filename.c_str(), cv::FileStorage::WRITE);
if (!fs.isOpened()) throw std::string("Could not open file ") + filename;
save(fs);
}
// --------------------------------------------------------------------------
void Database::save(cv::FileStorage &fs, const std::string &name) const {
// Format YAML:
// vocabulary { ... see TemplatedVocabulary::save }
// database
// {
// nEntries:
// usingDI:
// diLevels:
// invertedIndex
// [
// [
// {
// imageId:
// weight:
// }
// ]
// ]
// directIndex
// [
// [
// {
// nodeId:
// features: [ ]
// }
// ]
// ]
// invertedIndex[i] is for the i-th word
// directIndex[i] is for the i-th entry
// directIndex may be empty if not using direct index
//
// imageId's and nodeId's must be stored in ascending order
// (according to the construction of the indexes)
m_voc->save(fs);
fs << name << "{";
fs << "nEntries" << m_nentries;
fs << "usingDI" << (m_use_di ? 1 : 0);
fs << "diLevels" << m_dilevels;
fs << "invertedIndex"
<< "[";
for (auto iit = m_ifile.begin(); iit != m_ifile.end(); ++iit) {
fs << "["; // word of IF
for (auto irit = iit->begin(); irit != iit->end(); ++irit) {
fs << "{:"
<< "imageId" << (int)irit->entry_id << "weight" << irit->word_weight
<< "}";
}
fs << "]"; // word of IF
}
fs << "]"; // invertedIndex
fs << "directIndex"
<< "[";
for (auto dit = m_dfile.begin(); dit != m_dfile.end(); ++dit) {
fs << "["; // entry of DF
for (auto drit = dit->begin(); drit != dit->end(); ++drit) {
NodeId nid = drit->first;
const std::vector<unsigned int> &features = drit->second;
// save info of last_nid
fs << "{";
fs << "nodeId" << (int)nid;
// msvc++ 2010 with opencv 2.3.1 does not allow FileStorage::operator<<
// with vectors of unsigned int
fs << "features"
<< "[" << *(const std::vector<int> *)(&features) << "]";
fs << "}";
}
fs << "]"; // entry of DF
}
fs << "]"; // directIndex
fs << "}"; // database
}
// --------------------------------------------------------------------------
void Database::load(const std::string &filename) {
cv::FileStorage fs(filename.c_str(), cv::FileStorage::READ);
if (!fs.isOpened()) throw std::string("Could not open file ") + filename;
load(fs);
}
// --------------------------------------------------------------------------
void Database::load(const cv::FileStorage &fs, const std::string &name) {
// load voc first
// subclasses must instantiate m_voc before calling this ::load
if (!m_voc) m_voc = new Vocabulary;
m_voc->load(fs);
// load database now
clear(); // resizes inverted file
cv::FileNode fdb = fs[name];
m_nentries = (int)fdb["nEntries"];
m_use_di = (int)fdb["usingDI"] != 0;
m_dilevels = (int)fdb["diLevels"];
cv::FileNode fn = fdb["invertedIndex"];
for (WordId wid = 0; wid < fn.size(); ++wid) {
cv::FileNode fw = fn[wid];
for (unsigned int i = 0; i < fw.size(); ++i) {
EntryId eid = (int)fw[i]["imageId"];
WordValue v = fw[i]["weight"];
m_ifile[wid].push_back(IFPair(eid, v));
}
}
if (m_use_di) {
fn = fdb["directIndex"];
m_dfile.resize(fn.size());
assert(m_nentries == (int)fn.size());
FeatureVector::iterator dit;
for (EntryId eid = 0; eid < fn.size(); ++eid) {
cv::FileNode fe = fn[eid];
m_dfile[eid].clear();
for (unsigned int i = 0; i < fe.size(); ++i) {
NodeId nid = (int)fe[i]["nodeId"];
dit = m_dfile[eid].insert(m_dfile[eid].end(),
make_pair(nid, std::vector<unsigned int>()));
// this failed to compile with some opencv versions (2.3.1)
// fe[i]["features"] >> dit->second;
// this was ok until OpenCV 2.4.1
// std::vector<int> aux;
// fe[i]["features"] >> aux; // OpenCV < 2.4.1
// dit->second.resize(aux.size());
// std::copy(aux.begin(), aux.end(), dit->second.begin());
cv::FileNode ff = fe[i]["features"][0];
dit->second.reserve(ff.size());
cv::FileNodeIterator ffit;
for (ffit = ff.begin(); ffit != ff.end(); ++ffit) {
dit->second.push_back((int)*ffit);
}
}
} // for each entry
} // if use_id
}
std::ostream &operator<<(std::ostream &os, const Database &db) {
os << "Database: Entries = " << db.size() << ", "
"Using direct index = "
<< (db.usingDirectIndex() ? "yes" : "no");
if (db.usingDirectIndex())
os << ", Direct index levels = " << db.getDirectIndexLevels();
os << ". " << *db.getVocabulary();
return os;
}
} // namespace DBoW3

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thirdparty/DBoW3/src/Database.h vendored
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@ -1,354 +0,0 @@
/**
* File: Database.h
* Date: March 2011
* Modified By Rafael Muñoz in 2016
* Author: Dorian Galvez-Lopez
* Description: database of images
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_DATABASE__
#define __D_T_DATABASE__
#include <bitset>
#include <fstream>
#include <list>
#include <numeric>
#include <set>
#include <string>
#include <vector>
#include "BowVector.h"
#include "FeatureVector.h"
#include "QueryResults.h"
#include "ScoringObject.h"
#include "Vocabulary.h"
#include "exports.h"
namespace DBoW3 {
/// Database
class DBOW_API Database {
public:
/**
* Creates an empty database without vocabulary
* @param use_di a direct index is used to store feature indexes
* @param di_levels levels to go up the vocabulary tree to select the
* node id to store in the direct index when adding images
*/
explicit Database(bool use_di = true, int di_levels = 0);
/**
* Creates a database with the given vocabulary
* @param T class inherited from Vocabulary
* @param voc vocabulary
* @param use_di a direct index is used to store feature indexes
* @param di_levels levels to go up the vocabulary tree to select the
* node id to store in the direct index when adding images
*/
explicit Database(const Vocabulary &voc, bool use_di = true,
int di_levels = 0);
/**
* Copy constructor. Copies the vocabulary too
* @param db object to copy
*/
Database(const Database &db);
/**
* Creates the database from a file
* @param filename
*/
Database(const std::string &filename);
/**
* Creates the database from a file
* @param filename
*/
Database(const char *filename);
/**
* Destructor
*/
virtual ~Database(void);
/**
* Copies the given database and its vocabulary
* @param db database to copy
*/
Database &operator=(const Database &db);
/**
* Sets the vocabulary to use and clears the content of the database.
* @param T class inherited from Vocabulary
* @param voc vocabulary to copy
*/
void setVocabulary(const Vocabulary &voc);
/**
* Sets the vocabulary to use and the direct index parameters, and clears
* the content of the database
* @param T class inherited from Vocabulary
* @param voc vocabulary to copy
* @param use_di a direct index is used to store feature indexes
* @param di_levels levels to go up the vocabulary tree to select the
* node id to store in the direct index when adding images
*/
void setVocabulary(const Vocabulary &voc, bool use_di, int di_levels = 0);
/**
* Returns a pointer to the vocabulary used
* @return vocabulary
*/
inline const Vocabulary *getVocabulary() const { return m_voc; }
/**
* Allocates some memory for the direct and inverted indexes
* @param nd number of expected image entries in the database
* @param ni number of expected words per image
* @note Use 0 to ignore a parameter
*/
void allocate(int nd = 0, int ni = 0);
/**
* Adds an entry to the database and returns its index
* @param features features of the new entry
* @param bowvec if given, the bow vector of these features is returned
* @param fvec if given, the vector of nodes and feature indexes is returned
* @return id of new entry
*/
EntryId add(const std::vector<std::bitset<256>> &features,
BowVector *bowvec = NULL, FeatureVector *fvec = NULL);
/**
* Adds an entry to the database and returns its index
* @param features features of the new entry
* @param bowvec if given, the bow vector of these features is returned
* @param fvec if given, the vector of nodes and feature indexes is returned
* @return id of new entry
*/
EntryId add(const std::vector<cv::Mat> &features, BowVector *bowvec = NULL,
FeatureVector *fvec = NULL);
/**
* Adds an entry to the database and returns its index
* @param features features of the new entry, one per row
* @param bowvec if given, the bow vector of these features is returned
* @param fvec if given, the vector of nodes and feature indexes is returned
* @return id of new entry
*/
EntryId add(const cv::Mat &features, BowVector *bowvec = NULL,
FeatureVector *fvec = NULL);
/**
* Adss an entry to the database and returns its index
* @param vec bow vector
* @param fec feature vector to add the entry. Only necessary if using the
* direct index
* @return id of new entry
*/
EntryId add(const BowVector &vec, const FeatureVector &fec = FeatureVector());
/**
* Empties the database
*/
inline void clear();
/**
* Returns the number of entries in the database
* @return number of entries in the database
*/
unsigned int size() const { return m_nentries; }
/**
* Checks if the direct index is being used
* @return true iff using direct index
*/
bool usingDirectIndex() const { return m_use_di; }
/**
* Returns the di levels when using direct index
* @return di levels
*/
int getDirectIndexLevels() const { return m_dilevels; }
/**
* Queries the database with some features
* @param features query features
* @param ret (out) query results
* @param max_results number of results to return. <= 0 means all
* @param max_id only entries with id <= max_id are returned in ret.
* < 0 means all
*/
void query(const std::vector<cv::Mat> &features, QueryResults &ret,
int max_results = 1, int max_id = -1) const;
void query(const std::vector<std::bitset<256>> &features, QueryResults &ret,
int max_results = 1, int max_id = -1) const;
/**
* Queries the database with some features
* @param features query features,one per row
* @param ret (out) query results
* @param max_results number of results to return. <= 0 means all
* @param max_id only entries with id <= max_id are returned in ret.
* < 0 means all
*/
void query(const cv::Mat &features, QueryResults &ret, int max_results = 1,
int max_id = -1) const;
/**
* Queries the database with a vector
* @param vec bow vector already normalized
* @param ret results
* @param max_results number of results to return. <= 0 means all
* @param max_id only entries with id <= max_id are returned in ret.
* < 0 means all
*/
void query(const BowVector &vec, QueryResults &ret, int max_results = 1,
int max_id = -1) const;
/**
* Returns the a feature vector associated with a database entry
* @param id entry id (must be < size())
* @return const reference to map of nodes and their associated features in
* the given entry
*/
const FeatureVector &retrieveFeatures(EntryId id) const;
/**
* Stores the database in a file
* @param filename
*/
void save(const std::string &filename) const;
/**
* Loads the database from a file
* @param filename
*/
void load(const std::string &filename);
/**
* Stores the database in the given file storage structure
* @param fs
* @param name node name
*/
virtual void save(cv::FileStorage &fs,
const std::string &name = "database") const;
/**
* Loads the database from the given file storage structure
* @param fs
* @param name node name
*/
virtual void load(const cv::FileStorage &fs,
const std::string &name = "database");
// --------------------------------------------------------------------------
/**
* Writes printable information of the database
* @param os stream to write to
* @param db
*/
DBOW_API friend std::ostream &operator<<(std::ostream &os,
const Database &db);
protected:
/// Query with L1 scoring
void queryL1(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const;
/// Query with L2 scoring
void queryL2(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const;
/// Query with Chi square scoring
void queryChiSquare(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const;
/// Query with Bhattacharyya scoring
void queryBhattacharyya(const BowVector &vec, QueryResults &ret,
int max_results, int max_id) const;
/// Query with KL divergence scoring
void queryKL(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const;
/// Query with dot product scoring
void queryDotProduct(const BowVector &vec, QueryResults &ret, int max_results,
int max_id) const;
protected:
/* Inverted file declaration */
/// Item of IFRow
struct IFPair {
/// Entry id
EntryId entry_id;
/// Word weight in this entry
WordValue word_weight;
/**
* Creates an empty pair
*/
IFPair() {}
/**
* Creates an inverted file pair
* @param eid entry id
* @param wv word weight
*/
IFPair(EntryId eid, WordValue wv) : entry_id(eid), word_weight(wv) {}
/**
* Compares the entry ids
* @param eid
* @return true iff this entry id is the same as eid
*/
inline bool operator==(EntryId eid) const { return entry_id == eid; }
};
/// Row of InvertedFile
typedef std::vector<IFPair> IFRow;
// IFRows are sorted in ascending entry_id order
/// Inverted index
typedef std::vector<IFRow> InvertedFile;
// InvertedFile[word_id] --> inverted file of that word
/* Direct file declaration */
/// Direct index
typedef std::vector<FeatureVector> DirectFile;
// DirectFile[entry_id] --> [ directentry, ... ]
protected:
/// Associated vocabulary
Vocabulary *m_voc;
/// Flag to use direct index
bool m_use_di;
/// Levels to go up the vocabulary tree to select nodes to store
/// in the direct index
int m_dilevels;
/// Inverted file (must have size() == |words|)
InvertedFile m_ifile;
/// Direct file (resized for allocation)
DirectFile m_dfile;
/// Number of valid entries in m_dfile
int m_nentries;
};
// --------------------------------------------------------------------------
} // namespace DBoW3
#endif

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/**
* File: DescManip.cpp
* Date: June 2012
* Author: Dorian Galvez-Lopez
* Description: functions for ORB descriptors
* License: see the LICENSE.txt file
*
*/
#include <limits.h>
#include <stdint.h>
#include <iostream>
#include <sstream>
#include <string>
#include <vector>
#include "DescManip.h"
using namespace std;
namespace DBoW3 {
// --------------------------------------------------------------------------
void DescManip::meanValue(const std::vector<cv::Mat> &descriptors,
cv::Mat &mean) {
if (descriptors.empty()) return;
if (descriptors.size() == 1) {
mean = descriptors[0].clone();
return;
}
// binary descriptor
if (descriptors[0].type() == CV_8U) {
// determine number of bytes of the binary descriptor
int L = getDescSizeBytes(descriptors[0]);
vector<int> sum(L * 8, 0);
for (size_t i = 0; i < descriptors.size(); ++i) {
const cv::Mat &d = descriptors[i];
const unsigned char *p = d.ptr<unsigned char>();
for (int j = 0; j < d.cols; ++j, ++p) {
if (*p & (1 << 7)) ++sum[j * 8];
if (*p & (1 << 6)) ++sum[j * 8 + 1];
if (*p & (1 << 5)) ++sum[j * 8 + 2];
if (*p & (1 << 4)) ++sum[j * 8 + 3];
if (*p & (1 << 3)) ++sum[j * 8 + 4];
if (*p & (1 << 2)) ++sum[j * 8 + 5];
if (*p & (1 << 1)) ++sum[j * 8 + 6];
if (*p & (1)) ++sum[j * 8 + 7];
}
}
mean = cv::Mat::zeros(1, L, CV_8U);
unsigned char *p = mean.ptr<unsigned char>();
const int N2 = (int)descriptors.size() / 2 + descriptors.size() % 2;
for (size_t i = 0; i < sum.size(); ++i) {
if (sum[i] >= N2) {
// set bit
*p |= 1 << (7 - (i % 8));
}
if (i % 8 == 7) ++p;
}
}
// non binary descriptor
else {
assert(descriptors[0].type() == CV_32F); // ensure it is float
mean.create(1, descriptors[0].cols, descriptors[0].type());
mean.setTo(cv::Scalar::all(0));
float inv_s = 1. / double(descriptors.size());
for (size_t i = 0; i < descriptors.size(); i++)
mean += descriptors[i] * inv_s;
}
}
// --------------------------------------------------------------------------
// static inline uint32_t distance_8uc1(const cv::Mat &a, const cv::Mat &b);
double DescManip::distance(const cv::Mat &a, const cv::Mat &b) {
// binary descriptor
if (a.type() == CV_8U) {
// Bit count function got from:
// http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetKernighan
// This implementation assumes that a.cols (CV_8U) % sizeof(uint64_t) == 0
const uint64_t *pa, *pb;
pa = a.ptr<uint64_t>(); // a & b are actually CV_8U
pb = b.ptr<uint64_t>();
uint64_t v, ret = 0;
for (size_t i = 0; i < a.cols / sizeof(uint64_t); ++i, ++pa, ++pb) {
v = *pa ^ *pb;
v = v - ((v >> 1) & (uint64_t) ~(uint64_t)0 / 3);
v = (v & (uint64_t) ~(uint64_t)0 / 15 * 3) +
((v >> 2) & (uint64_t) ~(uint64_t)0 / 15 * 3);
v = (v + (v >> 4)) & (uint64_t) ~(uint64_t)0 / 255 * 15;
ret += (uint64_t)(v * ((uint64_t) ~(uint64_t)0 / 255)) >>
(sizeof(uint64_t) - 1) * CHAR_BIT;
}
return ret;
} else {
double sqd = 0.;
assert(a.type() == CV_32F);
assert(a.rows == 1);
const float *a_ptr = a.ptr<float>(0);
const float *b_ptr = b.ptr<float>(0);
for (int i = 0; i < a.cols; i++)
sqd += (a_ptr[i] - b_ptr[i]) * (a_ptr[i] - b_ptr[i]);
return sqd;
}
}
// --------------------------------------------------------------------------
std::string DescManip::toString(const cv::Mat &a) {
stringstream ss;
// introduce a magic value to distinguish from DBoW3
ss << "dbw3 ";
// save size and type
ss << a.type() << " " << a.cols << " ";
if (a.type() == CV_8U) {
const unsigned char *p = a.ptr<unsigned char>();
for (int i = 0; i < a.cols; ++i, ++p) ss << (int)*p << " ";
} else {
const float *p = a.ptr<float>();
for (int i = 0; i < a.cols; ++i, ++p) ss << *p << " ";
}
return ss.str();
}
// --------------------------------------------------------------------------
void DescManip::fromString(cv::Mat &a, const std::string &s) {
// check if the dbow3 is present
string ss_aux;
ss_aux.reserve(10);
for (size_t i = 0; i < 10 && i < s.size(); i++) ss_aux.push_back(s[i]);
if (ss_aux.find("dbw3") == std::string::npos) { // is DBoW3
// READ UNTIL END
stringstream ss(s);
int val;
vector<uchar> data;
data.reserve(100);
while (ss >> val) data.push_back(val);
// copy to a
a.create(1, data.size(), CV_8UC1);
memcpy(a.ptr<char>(0), &data[0], data.size());
} else {
int type, cols;
stringstream ss(s);
ss >> type >> cols;
a.create(1, cols, type);
if (type == CV_8UC1) {
unsigned char *p = a.ptr<unsigned char>();
int n;
for (int i = 0; i < a.cols; ++i, ++p)
if (ss >> n) *p = (unsigned char)n;
} else {
float *p = a.ptr<float>();
for (int i = 0; i < a.cols; ++i, ++p)
if (!(ss >> *p))
cerr << "Error reading. Unexpected EOF. DescManip::fromString"
<< endl;
}
}
}
// --------------------------------------------------------------------------
void DescManip::toMat32F(const std::vector<cv::Mat> &descriptors,
cv::Mat &mat) {
if (descriptors.empty()) {
mat.release();
return;
}
if (descriptors[0].type() == CV_8UC1) {
const size_t N = descriptors.size();
int L = getDescSizeBytes(descriptors[0]);
mat.create(N, L * 8, CV_32F);
float *p = mat.ptr<float>();
for (size_t i = 0; i < N; ++i) {
const int C = descriptors[i].cols;
const unsigned char *desc = descriptors[i].ptr<unsigned char>();
for (int j = 0; j < C; ++j, p += 8) {
p[0] = (desc[j] & (1 << 7) ? 1 : 0);
p[1] = (desc[j] & (1 << 6) ? 1 : 0);
p[2] = (desc[j] & (1 << 5) ? 1 : 0);
p[3] = (desc[j] & (1 << 4) ? 1 : 0);
p[4] = (desc[j] & (1 << 3) ? 1 : 0);
p[5] = (desc[j] & (1 << 2) ? 1 : 0);
p[6] = (desc[j] & (1 << 1) ? 1 : 0);
p[7] = desc[j] & (1);
}
}
} else {
assert(descriptors[0].type() == CV_32F);
const int N = descriptors.size();
int L = descriptors[0].cols;
mat.create(N, L, CV_32F);
for (int i = 0; i < N; ++i)
memcpy(mat.ptr<float>(i), descriptors[i].ptr<float>(0),
sizeof(float) * L);
}
}
void DescManip::toStream(const cv::Mat &m, std::ostream &str) {
assert(m.rows == 1 || m.isContinuous());
int type = m.type();
int cols = m.cols;
int rows = m.rows;
str.write((char *)&cols, sizeof(cols));
str.write((char *)&rows, sizeof(rows));
str.write((char *)&type, sizeof(type));
str.write((char *)m.ptr<char>(0), m.elemSize() * m.cols);
}
void DescManip::fromStream(cv::Mat &m, std::istream &str) {
int type, cols, rows;
str.read((char *)&cols, sizeof(cols));
str.read((char *)&rows, sizeof(rows));
str.read((char *)&type, sizeof(type));
m.create(rows, cols, type);
str.read((char *)m.ptr<char>(0), m.elemSize() * m.cols);
}
// --------------------------------------------------------------------------
} // namespace DBoW3

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/**
* File: FClass.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: generic FClass to instantiate templated classes
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_DESCMANIP__
#define __D_T_DESCMANIP__
#include <opencv2/core/core.hpp>
#include <string>
#include <vector>
#include "exports.h"
namespace DBoW3 {
/// Class to manipulate descriptors (calculating means, differences and IO
/// routines)
class DBOW_API DescManip {
public:
/**
* Calculates the mean value of a set of descriptors
* @param descriptors
* @param mean mean descriptor
*/
static void meanValue(const std::vector<cv::Mat> &descriptors, cv::Mat &mean);
/**
* Calculates the distance between two descriptors
* @param a
* @param b
* @return distance
*/
static double distance(const cv::Mat &a, const cv::Mat &b);
static inline uint32_t distance_8uc1(const cv::Mat &a, const cv::Mat &b);
/**
* Returns a string version of the descriptor
* @param a descriptor
* @return string version
*/
static std::string toString(const cv::Mat &a);
/**
* Returns a descriptor from a string
* @param a descriptor
* @param s string version
*/
static void fromString(cv::Mat &a, const std::string &s);
/**
* Returns a mat with the descriptors in float format
* @param descriptors
* @param mat (out) NxL 32F matrix
*/
static void toMat32F(const std::vector<cv::Mat> &descriptors, cv::Mat &mat);
/**io routines*/
static void toStream(const cv::Mat &m, std::ostream &str);
static void fromStream(cv::Mat &m, std::istream &str);
public:
/**Returns the number of bytes of the descriptor
* used for binary descriptors only*/
static size_t getDescSizeBytes(const cv::Mat &d) {
return d.cols * d.elemSize();
}
};
uint32_t DescManip::distance_8uc1(const cv::Mat &a, const cv::Mat &b) {
// binary descriptor
// Bit count function got from:
// http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetKernighan
// This implementation assumes that a.cols (CV_8U) % sizeof(uint64_t) == 0
const uint64_t *pa, *pb;
pa = a.ptr<uint64_t>(); // a & b are actually CV_8U
pb = b.ptr<uint64_t>();
uint64_t v, ret = 0;
size_t n = a.cols / sizeof(uint64_t);
for (size_t i = 0; i < n; ++i, ++pa, ++pb) {
v = *pa ^ *pb;
v = v - ((v >> 1) & (uint64_t) ~(uint64_t)0 / 3);
v = (v & (uint64_t) ~(uint64_t)0 / 15 * 3) +
((v >> 2) & (uint64_t) ~(uint64_t)0 / 15 * 3);
v = (v + (v >> 4)) & (uint64_t) ~(uint64_t)0 / 255 * 15;
ret += (uint64_t)(v * ((uint64_t) ~(uint64_t)0 / 255)) >>
(sizeof(uint64_t) - 1) * CHAR_BIT;
}
return ret;
}
} // namespace DBoW3
#endif

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/**
* File: FeatureVector.cpp
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: feature vector
* License: see the LICENSE.txt file
*
*/
#include "FeatureVector.h"
#include <map>
#include <vector>
#include <iostream>
namespace DBoW3 {
// ---------------------------------------------------------------------------
FeatureVector::FeatureVector(void)
{
}
// ---------------------------------------------------------------------------
FeatureVector::~FeatureVector(void)
{
}
// ---------------------------------------------------------------------------
void FeatureVector::addFeature(NodeId id, unsigned int i_feature)
{
FeatureVector::iterator vit = this->lower_bound(id);
if(vit != this->end() && vit->first == id)
{
vit->second.push_back(i_feature);
}
else
{
vit = this->insert(vit, FeatureVector::value_type(id,
std::vector<unsigned int>() ));
vit->second.push_back(i_feature);
}
}
// ---------------------------------------------------------------------------
std::ostream& operator<<(std::ostream &out,
const FeatureVector &v)
{
if(!v.empty())
{
FeatureVector::const_iterator vit = v.begin();
const std::vector<unsigned int>* f = &vit->second;
out << "<" << vit->first << ": [";
if(!f->empty()) out << (*f)[0];
for(unsigned int i = 1; i < f->size(); ++i)
{
out << ", " << (*f)[i];
}
out << "]>";
for(++vit; vit != v.end(); ++vit)
{
f = &vit->second;
out << ", <" << vit->first << ": [";
if(!f->empty()) out << (*f)[0];
for(unsigned int i = 1; i < f->size(); ++i)
{
out << ", " << (*f)[i];
}
out << "]>";
}
}
return out;
}
// ---------------------------------------------------------------------------
} // namespace DBoW3

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/**
* File: FeatureVector.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: feature vector
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_FEATURE_VECTOR__
#define __D_T_FEATURE_VECTOR__
#include "BowVector.h"
#include <map>
#include <vector>
#include "exports.h"
namespace DBoW3 {
/// Vector of nodes with indexes of local features
class DBOW_API FeatureVector:
public std::map<NodeId, std::vector<unsigned int> >
{
public:
/**
* Constructor
*/
FeatureVector(void);
/**
* Destructor
*/
~FeatureVector(void);
/**
* Adds a feature to an existing node, or adds a new node with an initial
* feature
* @param id node id to add or to modify
* @param i_feature index of feature to add to the given node
*/
void addFeature(NodeId id, unsigned int i_feature);
/**
* Sends a string versions of the feature vector through the stream
* @param out stream
* @param v feature vector
*/
friend std::ostream& operator<<(std::ostream &out, const FeatureVector &v);
};
} // namespace DBoW3
#endif

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/**
* File: QueryResults.cpp
* Date: March, November 2011
* Author: Dorian Galvez-Lopez
* Description: structure to store results of database queries
* License: see the LICENSE.txt file
*
*/
#include <iostream>
#include <fstream>
#include "QueryResults.h"
using namespace std;
namespace DBoW3
{
// ---------------------------------------------------------------------------
ostream & operator<<(ostream& os, const Result& ret )
{
os << "<EntryId: " << ret.Id << ", Score: " << ret.Score << ">";
return os;
}
// ---------------------------------------------------------------------------
ostream & operator<<(ostream& os, const QueryResults& ret )
{
if(ret.size() == 1)
os << "1 result:" << endl;
else
os << ret.size() << " results:" << endl;
QueryResults::const_iterator rit;
for(rit = ret.begin(); rit != ret.end(); ++rit)
{
os << *rit;
if(rit + 1 != ret.end()) os << endl;
}
return os;
}
// ---------------------------------------------------------------------------
void QueryResults::saveM(const std::string &filename) const
{
fstream f(filename.c_str(), ios::out);
QueryResults::const_iterator qit;
for(qit = begin(); qit != end(); ++qit)
{
f << qit->Id << " " << qit->Score << endl;
}
f.close();
}
// ---------------------------------------------------------------------------
} // namespace DBoW3

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/**
* File: QueryResults.h
* Date: March, November 2011
* Author: Dorian Galvez-Lopez
* Description: structure to store results of database queries
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_QUERY_RESULTS__
#define __D_T_QUERY_RESULTS__
#include <vector>
#include "exports.h"
namespace DBoW3 {
/// Id of entries of the database
typedef unsigned int EntryId;
/// Single result of a query
class DBOW_API Result
{
public:
/// Entry id
EntryId Id;
/// Score obtained
double Score;
/// debug
int nWords; // words in common
// !!! this is filled only by Bhatt score!
// (and for BCMatching, BCThresholding then)
double bhatScore, chiScore;
/// debug
// only done by ChiSq and BCThresholding
double sumCommonVi;
double sumCommonWi;
double expectedChiScore;
/// debug
/**
* Empty constructors
*/
inline Result(){}
/**
* Creates a result with the given data
* @param _id entry id
* @param _score score
*/
inline Result(EntryId _id, double _score): Id(_id), Score(_score){}
/**
* Compares the scores of two results
* @return true iff this.score < r.score
*/
inline bool operator<(const Result &r) const
{
return this->Score < r.Score;
}
/**
* Compares the scores of two results
* @return true iff this.score > r.score
*/
inline bool operator>(const Result &r) const
{
return this->Score > r.Score;
}
/**
* Compares the entry id of the result
* @return true iff this.id == id
*/
inline bool operator==(EntryId id) const
{
return this->Id == id;
}
/**
* Compares the score of this entry with a given one
* @param s score to compare with
* @return true iff this score < s
*/
inline bool operator<(double s) const
{
return this->Score < s;
}
/**
* Compares the score of this entry with a given one
* @param s score to compare with
* @return true iff this score > s
*/
inline bool operator>(double s) const
{
return this->Score > s;
}
/**
* Compares the score of two results
* @param a
* @param b
* @return true iff a.Score > b.Score
*/
static inline bool gt(const Result &a, const Result &b)
{
return a.Score > b.Score;
}
/**
* Compares the scores of two results
* @return true iff a.Score > b.Score
*/
inline static bool ge(const Result &a, const Result &b)
{
return a.Score > b.Score;
}
/**
* Returns true iff a.Score >= b.Score
* @param a
* @param b
* @return true iff a.Score >= b.Score
*/
static inline bool geq(const Result &a, const Result &b)
{
return a.Score >= b.Score;
}
/**
* Returns true iff a.Score >= s
* @param a
* @param s
* @return true iff a.Score >= s
*/
static inline bool geqv(const Result &a, double s)
{
return a.Score >= s;
}
/**
* Returns true iff a.Id < b.Id
* @param a
* @param b
* @return true iff a.Id < b.Id
*/
static inline bool ltId(const Result &a, const Result &b)
{
return a.Id < b.Id;
}
/**
* Prints a string version of the result
* @param os ostream
* @param ret Result to print
*/
friend std::ostream & operator<<(std::ostream& os, const Result& ret );
};
/// Multiple results from a query
class QueryResults: public std::vector<Result>
{
public:
/**
* Multiplies all the scores in the vector by factor
* @param factor
*/
inline void scaleScores(double factor);
/**
* Prints a string version of the results
* @param os ostream
* @param ret QueryResults to print
*/
DBOW_API friend std::ostream & operator<<(std::ostream& os, const QueryResults& ret );
/**
* Saves a matlab file with the results
* @param filename
*/
void saveM(const std::string &filename) const;
};
// --------------------------------------------------------------------------
inline void QueryResults::scaleScores(double factor)
{
for(QueryResults::iterator qit = begin(); qit != end(); ++qit)
qit->Score *= factor;
}
// --------------------------------------------------------------------------
} // namespace TemplatedBoW
#endif

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/**
* File: ScoringObject.cpp
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: functions to compute bow scores
* License: see the LICENSE.txt file
*
*/
#include <cfloat>
#include "Vocabulary.h"
#include "BowVector.h"
using namespace DBoW3;
// If you change the type of WordValue, make sure you change also the
// epsilon value (this is needed by the KL method)
const double GeneralScoring::LOG_EPS = log(DBL_EPSILON); // FLT_EPSILON
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double L1Scoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += fabs(vi - wi) - fabs(vi) - fabs(wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// ||v - w||_{L1} = 2 + Sum(|v_i - w_i| - |v_i| - |w_i|)
// for all i | v_i != 0 and w_i != 0
// (Nister, 2006)
// scaled_||v - w||_{L1} = 1 - 0.5 * ||v - w||_{L1}
score = -score/2.0;
return score; // [0..1]
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double L2Scoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += vi * wi;
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// ||v - w||_{L2} = sqrt( 2 - 2 * Sum(v_i * w_i) )
// for all i | v_i != 0 and w_i != 0 )
// (Nister, 2006)
if(score >= 1) // rounding errors
score = 1.0;
else
score = 1.0 - sqrt(1.0 - score); // [0..1]
return score;
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double ChiSquareScoring::score(const BowVector &v1, const BowVector &v2)
const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
// all the items are taken into account
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
// (v-w)^2/(v+w) - v - w = -4 vw/(v+w)
// we move the -4 out
if(vi + wi != 0.0) score += vi * wi / (vi + wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
}
}
// this takes the -4 into account
score = 2. * score; // [0..1]
return score;
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double KLScoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
// all the items or v are taken into account
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
if(vi != 0 && wi != 0) score += vi * log(vi/wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
score += vi * (log(vi) - LOG_EPS);
++v1_it;
}
else
{
// move v2_it forward, do not add any score
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// sum rest of items of v
for(; v1_it != v1_end; ++v1_it)
if(v1_it->second != 0)
score += v1_it->second * (log(v1_it->second) - LOG_EPS);
return score; // cannot be scaled
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double BhattacharyyaScoring::score(const BowVector &v1,
const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += sqrt(vi * wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
return score; // already scaled
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double DotProductScoring::score(const BowVector &v1,
const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += vi * wi;
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
return score; // cannot scale
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------

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thirdparty/DBoW3/src/ScoringObject.h vendored
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/**
* File: ScoringObject.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: functions to compute bow scores
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_SCORING_OBJECT__
#define __D_T_SCORING_OBJECT__
#include "BowVector.h"
#include "exports.h"
namespace DBoW3 {
/// Base class of scoring functions
class DBOW_API GeneralScoring
{
public:
/**
* Computes the score between two vectors. Vectors must be sorted and
* normalized if necessary
* @param v (in/out)
* @param w (in/out)
* @return score
*/
virtual double score(const BowVector &v, const BowVector &w) const = 0;
/**
* Returns whether a vector must be normalized before scoring according
* to the scoring scheme
* @param norm norm to use
* @return true iff must normalize
*/
virtual bool mustNormalize(LNorm &norm) const = 0;
/// Log of epsilon
static const double LOG_EPS;
// If you change the type of WordValue, make sure you change also the
// epsilon value (this is needed by the KL method)
virtual ~GeneralScoring() {} //!< Required for virtual base classes
};
/**
* Macro for defining Scoring classes
* @param NAME name of class
* @param MUSTNORMALIZE if vectors must be normalized to compute the score
* @param NORM type of norm to use when MUSTNORMALIZE
*/
#define __SCORING_CLASS(NAME, MUSTNORMALIZE, NORM) \
NAME: public GeneralScoring \
{ public: \
/** \
* Computes score between two vectors \
* @param v \
* @param w \
* @return score between v and w \
*/ \
virtual double score(const BowVector &v, const BowVector &w) const; \
\
/** \
* Says if a vector must be normalized according to the scoring function \
* @param norm (out) if true, norm to use
* @return true iff vectors must be normalized \
*/ \
virtual inline bool mustNormalize(LNorm &norm) const \
{ norm = NORM; return MUSTNORMALIZE; } \
}
/// L1 Scoring object
class __SCORING_CLASS(L1Scoring, true, L1);
/// L2 Scoring object
class __SCORING_CLASS(L2Scoring, true, L2);
/// Chi square Scoring object
class __SCORING_CLASS(ChiSquareScoring, true, L1);
/// KL divergence Scoring object
class __SCORING_CLASS(KLScoring, true, L1);
/// Bhattacharyya Scoring object
class __SCORING_CLASS(BhattacharyyaScoring, true, L1);
/// Dot product Scoring object
class __SCORING_CLASS(DotProductScoring, false, L1);
#undef __SCORING_CLASS
} // namespace DBoW3
#endif

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thirdparty/DBoW3/src/Vocabulary.h vendored
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/**
* File: Vocabulary.h
* Date: February 2011
* Author: Dorian Galvez-Lopez
* Description: templated vocabulary
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T__VOCABULARY__
#define __D_T__VOCABULARY__
#include <cassert>
#include <algorithm>
#include <bitset>
#include <fstream>
#include <iostream>
#include <limits>
#include <numeric>
#include <opencv2/core/core.hpp>
#include <string>
#include <vector>
#include "BowVector.h"
#include "FeatureVector.h"
#include "ScoringObject.h"
#include "exports.h"
namespace DBoW3 {
/// Vocabulary
class DBOW_API Vocabulary {
friend class FastSearch;
public:
/**
* Initiates an empty vocabulary
* @param k branching factor
* @param L depth levels
* @param weighting weighting type
* @param scoring scoring type
*/
Vocabulary(int k = 10, int L = 5, WeightingType weighting = TF_IDF,
ScoringType scoring = L1_NORM);
/**
* Creates the vocabulary by loading a file
* @param filename
*/
Vocabulary(const std::string &filename);
/**
* Creates the vocabulary by loading a file
* @param filename
*/
Vocabulary(const char *filename);
/**
* Copy constructor
* @param voc
*/
Vocabulary(const Vocabulary &voc);
/**
* Destructor
*/
virtual ~Vocabulary();
/**
* Assigns the given vocabulary to this by copying its data and removing
* all the data contained by this vocabulary before
* @param voc
* @return reference to this vocabulary
*/
Vocabulary &operator=(const Vocabulary &voc);
/**
* Creates a vocabulary from the training features with the already
* defined parameters
* @param training_features
*/
virtual void create(
const std::vector<std::vector<cv::Mat>> &training_features);
/**
* Creates a vocabulary from the training features with the already
* defined parameters
* @param training_features. Each row of a matrix is a feature
*/
virtual void create(const std::vector<cv::Mat> &training_features);
/**
* Creates a vocabulary from the training features, setting the branching
* factor and the depth levels of the tree
* @param training_features
* @param k branching factor
* @param L depth levels
*/
virtual void create(
const std::vector<std::vector<cv::Mat>> &training_features, int k, int L);
/**
* Creates a vocabulary from the training features, setting the branching
* factor nad the depth levels of the tree, and the weighting and scoring
* schemes
*/
virtual void create(
const std::vector<std::vector<cv::Mat>> &training_features, int k, int L,
WeightingType weighting, ScoringType scoring);
/**
* Returns the number of words in the vocabulary
* @return number of words
*/
virtual inline unsigned int size() const {
return (unsigned int)m_words.size();
}
/**
* Returns whether the vocabulary is empty (i.e. it has not been trained)
* @return true iff the vocabulary is empty
*/
virtual inline bool empty() const { return m_words.empty(); }
/** Clears the vocabulary object
*/
void clear();
/**
* Transforms a set of descriptores into a bow vector
* @param features
* @param v (out) bow vector of weighted words
*/
virtual void transform(const std::vector<cv::Mat> &features,
BowVector &v) const;
virtual void transform(const std::vector<std::bitset<256>> &features,
BowVector &v) const;
/**
* Transforms a set of descriptores into a bow vector
* @param features, one per row
* @param v (out) bow vector of weighted words
*/
virtual void transform(const cv::Mat &features, BowVector &v) const;
/**
* Transform a set of descriptors into a bow vector and a feature vector
* @param features
* @param v (out) bow vector
* @param fv (out) feature vector of nodes and feature indexes
* @param levelsup levels to go up the vocabulary tree to get the node index
*/
virtual void transform(const std::vector<cv::Mat> &features, BowVector &v,
FeatureVector &fv, int levelsup) const;
/**
* Transforms a single feature into a word (without weight)
* @param feature
* @return word id
*/
virtual WordId transform(const cv::Mat &feature) const;
void transform(const std::vector<std::bitset<256>> &features, BowVector &v,
FeatureVector &fv, int levelsup) const;
/**
* Returns the score of two vectors
* @param a vector
* @param b vector
* @return score between vectors
* @note the vectors must be already sorted and normalized if necessary
*/
double score(const BowVector &a, const BowVector &b) const {
return m_scoring_object->score(a, b);
}
/**
* Returns the id of the node that is "levelsup" levels from the word given
* @param wid word id
* @param levelsup 0..L
* @return node id. if levelsup is 0, returns the node id associated to the
* word id
*/
virtual NodeId getParentNode(WordId wid, int levelsup) const;
/**
* Returns the ids of all the words that are under the given node id,
* by traversing any of the branches that goes down from the node
* @param nid starting node id
* @param words ids of words
*/
void getWordsFromNode(NodeId nid, std::vector<WordId> &words) const;
/**
* Returns the branching factor of the tree (k)
* @return k
*/
inline int getBranchingFactor() const { return m_k; }
/**
* Returns the depth levels of the tree (L)
* @return L
*/
inline int getDepthLevels() const { return m_L; }
/**
* Returns the real depth levels of the tree on average
* @return average of depth levels of leaves
*/
float getEffectiveLevels() const;
/**
* Returns the descriptor of a word
* @param wid word id
* @return descriptor
*/
virtual inline cv::Mat getWord(WordId wid) const;
/**
* Returns the weight of a word
* @param wid word id
* @return weight
*/
virtual inline WordValue getWordWeight(WordId wid) const;
/**
* Returns the weighting method
* @return weighting method
*/
inline WeightingType getWeightingType() const { return m_weighting; }
/**
* Returns the scoring method
* @return scoring method
*/
inline ScoringType getScoringType() const { return m_scoring; }
/**
* Changes the weighting method
* @param type new weighting type
*/
inline void setWeightingType(WeightingType type);
/**
* Changes the scoring method
* @param type new scoring type
*/
void setScoringType(ScoringType type);
/**
* Saves the vocabulary into a file. If filename extension contains .yml,
* opencv YALM format is used. Otherwise, binary format is employed
* @param filename
*/
void save(const std::string &filename, bool binary_compressed = true) const;
/**
* Loads the vocabulary from a file created with save
* @param filename.
*/
void load(const std::string &filename);
/**
* Saves the vocabulary to a file storage structure
* @param fn node in file storage
*/
virtual void save(cv::FileStorage &fs,
const std::string &name = "vocabulary") const;
/**
* Loads the vocabulary from a file storage node
* @param fn first node
* @param subname name of the child node of fn where the tree is stored.
* If not given, the fn node is used instead
*/
virtual void load(const cv::FileStorage &fs,
const std::string &name = "vocabulary");
/**
* Stops those words whose weight is below minWeight.
* Words are stopped by setting their weight to 0. There are not returned
* later when transforming image features into vectors.
* Note that when using IDF or TF_IDF, the weight is the idf part, which
* is equivalent to -log(f), where f is the frequency of the word
* (f = Ni/N, Ni: number of training images where the word is present,
* N: number of training images).
* Note that the old weight is forgotten, and subsequent calls to this
* function with a lower minWeight have no effect.
* @return number of words stopped now
*/
virtual int stopWords(double minWeight);
/** Returns the size of the descriptor employed. If the Vocabulary is empty,
* returns -1
*/
int getDescritorSize() const;
/** Returns the type of the descriptor employed normally(8U_C1, 32F_C1)
*/
int getDescritorType() const;
// io to-from a stream
void toStream(std::ostream &str, bool compressed = true) const;
void fromStream(std::istream &str);
protected:
/// reference to descriptor
typedef const cv::Mat pDescriptor;
/// Tree node
struct Node {
/// Node id
NodeId id;
/// Weight if the node is a word
WordValue weight;
/// Children
std::vector<NodeId> children;
/// Parent node (undefined in case of root)
NodeId parent;
/// Node descriptor
cv::Mat descriptor;
/// Word id if the node is a word
WordId word_id;
/**
* Empty constructor
*/
Node() : id(0), weight(0), parent(0), word_id(0) {}
/**
* Constructor
* @param _id node id
*/
Node(NodeId _id) : id(_id), weight(0), parent(0), word_id(0) {}
/**
* Returns whether the node is a leaf node
* @return true iff the node is a leaf
*/
inline bool isLeaf() const { return children.empty(); }
};
protected:
/**
* Creates an instance of the scoring object accoring to m_scoring
*/
void createScoringObject();
/**
* Returns a set of pointers to descriptores
* @param training_features all the features
* @param features (out) pointers to the training features
*/
void getFeatures(const std::vector<std::vector<cv::Mat>> &training_features,
std::vector<cv::Mat> &features) const;
/**
* Returns the word id associated to a feature
* @param feature
* @param id (out) word id
* @param weight (out) word weight
* @param nid (out) if given, id of the node "levelsup" levels up
* @param levelsup
*/
virtual void transform(const cv::Mat &feature, WordId &id, WordValue &weight,
NodeId *nid, int levelsup = 0) const;
/**
* Returns the word id associated to a feature
* @param feature
* @param id (out) word id
* @param weight (out) word weight
* @param nid (out) if given, id of the node "levelsup" levels up
* @param levelsup
*/
virtual void transform(const cv::Mat &feature, WordId &id,
WordValue &weight) const;
void transform(const std::bitset<256> &feature, WordId &word_id,
WordValue &weight) const;
virtual void transform(const std::bitset<256> &feature, WordId &word_id,
WordValue &weight, NodeId *nid, int levelsup) const;
/**
* Returns the word id associated to a feature
* @param feature
* @param id (out) word id
*/
virtual void transform(const cv::Mat &feature, WordId &id) const;
/**
* Creates a level in the tree, under the parent, by running kmeans with
* a descriptor set, and recursively creates the subsequent levels too
* @param parent_id id of parent node
* @param descriptors descriptors to run the kmeans on
* @param current_level current level in the tree
*/
void HKmeansStep(NodeId parent_id, const std::vector<cv::Mat> &descriptors,
int current_level);
/**
* Creates k clusters from the given descriptors with some seeding algorithm.
* @note In this class, kmeans++ is used, but this function should be
* overriden by inherited classes.
*/
virtual void initiateClusters(const std::vector<cv::Mat> &descriptors,
std::vector<cv::Mat> &clusters) const;
/**
* Creates k clusters from the given descriptor sets by running the
* initial step of kmeans++
* @param descriptors
* @param clusters resulting clusters
*/
void initiateClustersKMpp(const std::vector<cv::Mat> &descriptors,
std::vector<cv::Mat> &clusters) const;
/**
* Create the words of the vocabulary once the tree has been built
*/
void createWords();
/**
* Sets the weights of the nodes of tree according to the given features.
* Before calling this function, the nodes and the words must be already
* created (by calling HKmeansStep and createWords)
* @param features
*/
void setNodeWeights(const std::vector<std::vector<cv::Mat>> &features);
/**
* Writes printable information of the vocabulary
* @param os stream to write to
* @param voc
*/
DBOW_API friend std::ostream &operator<<(std::ostream &os,
const Vocabulary &voc);
/**Loads from ORBSLAM txt files
*/
void load_fromtxt(const std::string &filename);
protected:
/// Branching factor
int m_k;
/// Depth levels
int m_L;
/// Weighting method
WeightingType m_weighting;
/// Scoring method
ScoringType m_scoring;
/// Object for computing scores
GeneralScoring *m_scoring_object;
/// Tree nodes
std::vector<Node> m_nodes;
/// Words of the vocabulary (tree leaves)
/// this condition holds: m_words[wid]->word_id == wid
std::vector<Node *> m_words;
public:
// for debug (REMOVE)
inline Node *getNodeWord(uint32_t idx) { return m_words[idx]; }
};
} // namespace DBoW3
#endif

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thirdparty/DBoW3/src/exports.h vendored
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@ -1,51 +0,0 @@
/*****************************
Copyright 2014 Rafael Muñoz Salinas. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are
permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list
of conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
THIS SOFTWARE IS PROVIDED BY Rafael Muñoz Salinas ''AS IS'' AND ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL Rafael Muñoz Salinas OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those of the
authors and should not be interpreted as representing official policies, either expressed
or implied, of Rafael Muñoz Salinas.
********************************/
#ifndef __DBOW_CORE_TYPES_H__
#define __DBOW_CORE_TYPES_H__
#if !defined _CRT_SECURE_NO_DEPRECATE && _MSC_VER > 1300
#define _CRT_SECURE_NO_DEPRECATE /* to avoid multiple Visual Studio 2005 warnings */
#endif
#if (defined WIN32 || defined _WIN32 || defined WINCE) && defined DBOW_DSO_EXPORTS
#define DBOW_API __declspec(dllexport)
#pragma warning ( disable : 4251 ) //disable warning to templates with dll linkage.
#pragma warning ( disable : 4290 ) //disable warning due to exception specifications.
#pragma warning ( disable : 4996 ) //disable warning regarding unsafe vsprintf.
#pragma warning ( disable : 4244 ) //disable warning convesions with lost of data.
#else
#define DBOW_API
#endif
#define DBOW_VERSION "3.0.0"
#endif

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// Fast data compression library
// Copyright (C) 2006-2011 Lasse Mikkel Reinhold
// lar@quicklz.com
//
// QuickLZ can be used for free under the GPL 1, 2 or 3 license (where anything
// released into public must be open source) or under a commercial license if such
// has been acquired (see http://www.quicklz.com/order.html). The commercial license
// does not cover derived or ported versions created by third parties under GPL.
// 1.5.0 final
#include "quicklz.h"
#if QLZ_VERSION_MAJOR != 1 || QLZ_VERSION_MINOR != 5 || QLZ_VERSION_REVISION != 0
#error quicklz.c and quicklz.h have different versions
#endif
#if (defined(__X86__) || defined(__i386__) || defined(i386) || defined(_M_IX86) || defined(__386__) || defined(__x86_64__) || defined(_M_X64))
#define X86X64
#endif
#define MINOFFSET 2
#define UNCONDITIONAL_MATCHLEN 6
#define UNCOMPRESSED_END 4
#define CWORD_LEN 4
#if QLZ_COMPRESSION_LEVEL == 1 && defined QLZ_PTR_64 && QLZ_STREAMING_BUFFER == 0
#define OFFSET_BASE source
#define CAST (ui32)(size_t)
#else
#define OFFSET_BASE 0
#define CAST
#endif
int qlz_get_setting(int setting)
{
switch (setting)
{
case 0: return QLZ_COMPRESSION_LEVEL;
case 1: return sizeof(qlz_state_compress);
case 2: return sizeof(qlz_state_decompress);
case 3: return QLZ_STREAMING_BUFFER;
#ifdef QLZ_MEMORY_SAFE
case 6: return 1;
#else
case 6: return 0;
#endif
case 7: return QLZ_VERSION_MAJOR;
case 8: return QLZ_VERSION_MINOR;
case 9: return QLZ_VERSION_REVISION;
}
return -1;
}
#if QLZ_COMPRESSION_LEVEL == 1
static int same(const unsigned char *src, size_t n)
{
while(n > 0 && *(src + n) == *src)
n--;
return n == 0 ? 1 : 0;
}
#endif
static void reset_table_compress(qlz_state_compress *state)
{
int i;
for(i = 0; i < QLZ_HASH_VALUES; i++)
{
#if QLZ_COMPRESSION_LEVEL == 1
state->hash[i].offset = 0;
#else
state->hash_counter[i] = 0;
#endif
}
}
static void reset_table_decompress(qlz_state_decompress *state)
{
int i;
(void)state;
(void)i;
#if QLZ_COMPRESSION_LEVEL == 2
for(i = 0; i < QLZ_HASH_VALUES; i++)
{
state->hash_counter[i] = 0;
}
#endif
}
static __inline ui32 hash_func(ui32 i)
{
#if QLZ_COMPRESSION_LEVEL == 2
return ((i >> 9) ^ (i >> 13) ^ i) & (QLZ_HASH_VALUES - 1);
#else
return ((i >> 12) ^ i) & (QLZ_HASH_VALUES - 1);
#endif
}
static __inline ui32 fast_read(void const *src, ui32 bytes)
{
#ifndef X86X64
unsigned char *p = (unsigned char*)src;
switch (bytes)
{
case 4:
return(*p | *(p + 1) << 8 | *(p + 2) << 16 | *(p + 3) << 24);
case 3:
return(*p | *(p + 1) << 8 | *(p + 2) << 16);
case 2:
return(*p | *(p + 1) << 8);
case 1:
return(*p);
}
return 0;
#else
if (bytes >= 1 && bytes <= 4)
return *((ui32*)src);
else
return 0;
#endif
}
static __inline ui32 hashat(const unsigned char *src)
{
ui32 fetch, hash;
fetch = fast_read(src, 3);
hash = hash_func(fetch);
return hash;
}
static __inline void fast_write(ui32 f, void *dst, size_t bytes)
{
#ifndef X86X64
unsigned char *p = (unsigned char*)dst;
switch (bytes)
{
case 4:
*p = (unsigned char)f;
*(p + 1) = (unsigned char)(f >> 8);
*(p + 2) = (unsigned char)(f >> 16);
*(p + 3) = (unsigned char)(f >> 24);
return;
case 3:
*p = (unsigned char)f;
*(p + 1) = (unsigned char)(f >> 8);
*(p + 2) = (unsigned char)(f >> 16);
return;
case 2:
*p = (unsigned char)f;
*(p + 1) = (unsigned char)(f >> 8);
return;
case 1:
*p = (unsigned char)f;
return;
}
#else
switch (bytes)
{
case 4:
*((ui32*)dst) = f;
return;
case 3:
*((ui32*)dst) = f;
return;
case 2:
*((ui16 *)dst) = (ui16)f;
return;
case 1:
*((unsigned char*)dst) = (unsigned char)f;
return;
}
#endif
}
size_t qlz_size_decompressed(const char *source)
{
ui32 n, r;
n = (((*source) & 2) == 2) ? 4 : 1;
r = fast_read(source + 1 + n, n);
r = r & (0xffffffff >> ((4 - n)*8));
return r;
}
size_t qlz_size_compressed(const char *source)
{
ui32 n, r;
n = (((*source) & 2) == 2) ? 4 : 1;
r = fast_read(source + 1, n);
r = r & (0xffffffff >> ((4 - n)*8));
return r;
}
size_t qlz_size_header(const char *source)
{
size_t n = 2*((((*source) & 2) == 2) ? 4 : 1) + 1;
return n;
}
static __inline void memcpy_up(unsigned char *dst, const unsigned char *src, ui32 n)
{
// Caution if modifying memcpy_up! Overlap of dst and src must be special handled.
#ifndef X86X64
unsigned char *end = dst + n;
while(dst < end)
{
*dst = *src;
dst++;
src++;
}
#else
ui32 f = 0;
do
{
*(ui32 *)(dst + f) = *(ui32 *)(src + f);
f += MINOFFSET + 1;
}
while (f < n);
#endif
}
static __inline void update_hash(qlz_state_decompress *state, const unsigned char *s)
{
#if QLZ_COMPRESSION_LEVEL == 1
ui32 hash;
hash = hashat(s);
state->hash[hash].offset = s;
state->hash_counter[hash] = 1;
#elif QLZ_COMPRESSION_LEVEL == 2
ui32 hash;
unsigned char c;
hash = hashat(s);
c = state->hash_counter[hash];
state->hash[hash].offset[c & (QLZ_POINTERS - 1)] = s;
c++;
state->hash_counter[hash] = c;
#endif
(void)state;
(void)s;
}
#if QLZ_COMPRESSION_LEVEL <= 2
static void update_hash_upto(qlz_state_decompress *state, unsigned char **lh, const unsigned char *max)
{
while(*lh < max)
{
(*lh)++;
update_hash(state, *lh);
}
}
#endif
static size_t qlz_compress_core(const unsigned char *source, unsigned char *destination, size_t size, qlz_state_compress *state)
{
const unsigned char *last_byte = source + size - 1;
const unsigned char *src = source;
unsigned char *cword_ptr = destination;
unsigned char *dst = destination + CWORD_LEN;
ui32 cword_val = 1U << 31;
const unsigned char *last_matchstart = last_byte - UNCONDITIONAL_MATCHLEN - UNCOMPRESSED_END;
ui32 fetch = 0;
unsigned int lits = 0;
(void) lits;
if(src <= last_matchstart)
fetch = fast_read(src, 3);
while(src <= last_matchstart)
{
if ((cword_val & 1) == 1)
{
// store uncompressed if compression ratio is too low
if (src > source + (size >> 1) && dst - destination > src - source - ((src - source) >> 5))
return 0;
fast_write((cword_val >> 1) | (1U << 31), cword_ptr, CWORD_LEN);
cword_ptr = dst;
dst += CWORD_LEN;
cword_val = 1U << 31;
fetch = fast_read(src, 3);
}
#if QLZ_COMPRESSION_LEVEL == 1
{
const unsigned char *o;
ui32 hash, cached;
hash = hash_func(fetch);
cached = fetch ^ state->hash[hash].cache;
state->hash[hash].cache = fetch;
o = state->hash[hash].offset + OFFSET_BASE;
state->hash[hash].offset = CAST(src - OFFSET_BASE);
#ifdef X86X64
if ((cached & 0xffffff) == 0 && o != OFFSET_BASE && (src - o > MINOFFSET || (src == o + 1 && lits >= 3 && src > source + 3 && same(src - 3, 6))))
{
if(cached != 0)
{
#else
if (cached == 0 && o != OFFSET_BASE && (src - o > MINOFFSET || (src == o + 1 && lits >= 3 && src > source + 3 && same(src - 3, 6))))
{
if (*(o + 3) != *(src + 3))
{
#endif
hash <<= 4;
cword_val = (cword_val >> 1) | (1U << 31);
fast_write((3 - 2) | hash, dst, 2);
src += 3;
dst += 2;
}
else
{
const unsigned char *old_src = src;
size_t matchlen;
hash <<= 4;
cword_val = (cword_val >> 1) | (1U << 31);
src += 4;
if(*(o + (src - old_src)) == *src)
{
src++;
if(*(o + (src - old_src)) == *src)
{
size_t q = last_byte - UNCOMPRESSED_END - (src - 5) + 1;
size_t remaining = q > 255 ? 255 : q;
src++;
while(*(o + (src - old_src)) == *src && (size_t)(src - old_src) < remaining)
src++;
}
}
matchlen = src - old_src;
if (matchlen < 18)
{
fast_write((ui32)(matchlen - 2) | hash, dst, 2);
dst += 2;
}
else
{
fast_write((ui32)(matchlen << 16) | hash, dst, 3);
dst += 3;
}
}
fetch = fast_read(src, 3);
lits = 0;
}
else
{
lits++;
*dst = *src;
src++;
dst++;
cword_val = (cword_val >> 1);
#ifdef X86X64
fetch = fast_read(src, 3);
#else
fetch = (fetch >> 8 & 0xffff) | (*(src + 2) << 16);
#endif
}
}
#elif QLZ_COMPRESSION_LEVEL >= 2
{
const unsigned char *o, *offset2;
ui32 hash, matchlen, k, m, best_k = 0;
unsigned char c;
size_t remaining = (last_byte - UNCOMPRESSED_END - src + 1) > 255 ? 255 : (last_byte - UNCOMPRESSED_END - src + 1);
(void)best_k;
//hash = hashat(src);
fetch = fast_read(src, 3);
hash = hash_func(fetch);
c = state->hash_counter[hash];
offset2 = state->hash[hash].offset[0];
if(offset2 < src - MINOFFSET && c > 0 && ((fast_read(offset2, 3) ^ fetch) & 0xffffff) == 0)
{
matchlen = 3;
if(*(offset2 + matchlen) == *(src + matchlen))
{
matchlen = 4;
while(*(offset2 + matchlen) == *(src + matchlen) && matchlen < remaining)
matchlen++;
}
}
else
matchlen = 0;
for(k = 1; k < QLZ_POINTERS && c > k; k++)
{
o = state->hash[hash].offset[k];
#if QLZ_COMPRESSION_LEVEL == 3
if(((fast_read(o, 3) ^ fetch) & 0xffffff) == 0 && o < src - MINOFFSET)
#elif QLZ_COMPRESSION_LEVEL == 2
if(*(src + matchlen) == *(o + matchlen) && ((fast_read(o, 3) ^ fetch) & 0xffffff) == 0 && o < src - MINOFFSET)
#endif
{
m = 3;
while(*(o + m) == *(src + m) && m < remaining)
m++;
#if QLZ_COMPRESSION_LEVEL == 3
if ((m > matchlen) || (m == matchlen && o > offset2))
#elif QLZ_COMPRESSION_LEVEL == 2
if (m > matchlen)
#endif
{
offset2 = o;
matchlen = m;
best_k = k;
}
}
}
o = offset2;
state->hash[hash].offset[c & (QLZ_POINTERS - 1)] = src;
c++;
state->hash_counter[hash] = c;
#if QLZ_COMPRESSION_LEVEL == 3
if(matchlen > 2 && src - o < 131071)
{
ui32 u;
size_t offset = src - o;
for(u = 1; u < matchlen; u++)
{
hash = hashat(src + u);
c = state->hash_counter[hash]++;
state->hash[hash].offset[c & (QLZ_POINTERS - 1)] = src + u;
}
cword_val = (cword_val >> 1) | (1U << 31);
src += matchlen;
if(matchlen == 3 && offset <= 63)
{
*dst = (unsigned char)(offset << 2);
dst++;
}
else if (matchlen == 3 && offset <= 16383)
{
ui32 f = (ui32)((offset << 2) | 1);
fast_write(f, dst, 2);
dst += 2;
}
else if (matchlen <= 18 && offset <= 1023)
{
ui32 f = ((matchlen - 3) << 2) | ((ui32)offset << 6) | 2;
fast_write(f, dst, 2);
dst += 2;
}
else if(matchlen <= 33)
{
ui32 f = ((matchlen - 2) << 2) | ((ui32)offset << 7) | 3;
fast_write(f, dst, 3);
dst += 3;
}
else
{
ui32 f = ((matchlen - 3) << 7) | ((ui32)offset << 15) | 3;
fast_write(f, dst, 4);
dst += 4;
}
}
else
{
*dst = *src;
src++;
dst++;
cword_val = (cword_val >> 1);
}
#elif QLZ_COMPRESSION_LEVEL == 2
if(matchlen > 2)
{
cword_val = (cword_val >> 1) | (1U << 31);
src += matchlen;
if (matchlen < 10)
{
ui32 f = best_k | ((matchlen - 2) << 2) | (hash << 5);
fast_write(f, dst, 2);
dst += 2;
}
else
{
ui32 f = best_k | (matchlen << 16) | (hash << 5);
fast_write(f, dst, 3);
dst += 3;
}
}
else
{
*dst = *src;
src++;
dst++;
cword_val = (cword_val >> 1);
}
#endif
}
#endif
}
while (src <= last_byte)
{
if ((cword_val & 1) == 1)
{
fast_write((cword_val >> 1) | (1U << 31), cword_ptr, CWORD_LEN);
cword_ptr = dst;
dst += CWORD_LEN;
cword_val = 1U << 31;
}
#if QLZ_COMPRESSION_LEVEL < 3
if (src <= last_byte - 3)
{
#if QLZ_COMPRESSION_LEVEL == 1
ui32 hash, fetch;
fetch = fast_read(src, 3);
hash = hash_func(fetch);
state->hash[hash].offset = CAST(src - OFFSET_BASE);
state->hash[hash].cache = fetch;
#elif QLZ_COMPRESSION_LEVEL == 2
ui32 hash;
unsigned char c;
hash = hashat(src);
c = state->hash_counter[hash];
state->hash[hash].offset[c & (QLZ_POINTERS - 1)] = src;
c++;
state->hash_counter[hash] = c;
#endif
}
#endif
*dst = *src;
src++;
dst++;
cword_val = (cword_val >> 1);
}
while((cword_val & 1) != 1)
cword_val = (cword_val >> 1);
fast_write((cword_val >> 1) | (1U << 31), cword_ptr, CWORD_LEN);
// min. size must be 9 bytes so that the qlz_size functions can take 9 bytes as argument
return dst - destination < 9 ? 9 : dst - destination;
}
static size_t qlz_decompress_core(const unsigned char *source, unsigned char *destination, size_t size, qlz_state_decompress *state, const unsigned char *history)
{
const unsigned char *src = source + qlz_size_header((const char *)source);
unsigned char *dst = destination;
const unsigned char *last_destination_byte = destination + size - 1;
ui32 cword_val = 1;
const unsigned char *last_matchstart = last_destination_byte - UNCONDITIONAL_MATCHLEN - UNCOMPRESSED_END;
unsigned char *last_hashed = destination - 1;
const unsigned char *last_source_byte = source + qlz_size_compressed((const char *)source) - 1;
static const ui32 bitlut[16] = {4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0};
(void) last_source_byte;
(void) last_hashed;
(void) state;
(void) history;
for(;;)
{
ui32 fetch;
if (cword_val == 1)
{
#ifdef QLZ_MEMORY_SAFE
if(src + CWORD_LEN - 1 > last_source_byte)
return 0;
#endif
cword_val = fast_read(src, CWORD_LEN);
src += CWORD_LEN;
}
#ifdef QLZ_MEMORY_SAFE
if(src + 4 - 1 > last_source_byte)
return 0;
#endif
fetch = fast_read(src, 4);
if ((cword_val & 1) == 1)
{
ui32 matchlen;
const unsigned char *offset2;
#if QLZ_COMPRESSION_LEVEL == 1
ui32 hash;
cword_val = cword_val >> 1;
hash = (fetch >> 4) & 0xfff;
offset2 = (const unsigned char *)(size_t)state->hash[hash].offset;
if((fetch & 0xf) != 0)
{
matchlen = (fetch & 0xf) + 2;
src += 2;
}
else
{
matchlen = *(src + 2);
src += 3;
}
#elif QLZ_COMPRESSION_LEVEL == 2
ui32 hash;
unsigned char c;
cword_val = cword_val >> 1;
hash = (fetch >> 5) & 0x7ff;
c = (unsigned char)(fetch & 0x3);
offset2 = state->hash[hash].offset[c];
if((fetch & (28)) != 0)
{
matchlen = ((fetch >> 2) & 0x7) + 2;
src += 2;
}
else
{
matchlen = *(src + 2);
src += 3;
}
#elif QLZ_COMPRESSION_LEVEL == 3
ui32 offset;
cword_val = cword_val >> 1;
if ((fetch & 3) == 0)
{
offset = (fetch & 0xff) >> 2;
matchlen = 3;
src++;
}
else if ((fetch & 2) == 0)
{
offset = (fetch & 0xffff) >> 2;
matchlen = 3;
src += 2;
}
else if ((fetch & 1) == 0)
{
offset = (fetch & 0xffff) >> 6;
matchlen = ((fetch >> 2) & 15) + 3;
src += 2;
}
else if ((fetch & 127) != 3)
{
offset = (fetch >> 7) & 0x1ffff;
matchlen = ((fetch >> 2) & 0x1f) + 2;
src += 3;
}
else
{
offset = (fetch >> 15);
matchlen = ((fetch >> 7) & 255) + 3;
src += 4;
}
offset2 = dst - offset;
#endif
#ifdef QLZ_MEMORY_SAFE
if(offset2 < history || offset2 > dst - MINOFFSET - 1)
return 0;
if(matchlen > (ui32)(last_destination_byte - dst - UNCOMPRESSED_END + 1))
return 0;
#endif
memcpy_up(dst, offset2, matchlen);
dst += matchlen;
#if QLZ_COMPRESSION_LEVEL <= 2
update_hash_upto(state, &last_hashed, dst - matchlen);
last_hashed = dst - 1;
#endif
}
else
{
if (dst < last_matchstart)
{
unsigned int n = bitlut[cword_val & 0xf];
#ifdef X86X64
*(ui32 *)dst = *(ui32 *)src;
#else
memcpy_up(dst, src, 4);
#endif
cword_val = cword_val >> n;
dst += n;
src += n;
#if QLZ_COMPRESSION_LEVEL <= 2
update_hash_upto(state, &last_hashed, dst - 3);
#endif
}
else
{
while(dst <= last_destination_byte)
{
if (cword_val == 1)
{
src += CWORD_LEN;
cword_val = 1U << 31;
}
#ifdef QLZ_MEMORY_SAFE
if(src >= last_source_byte + 1)
return 0;
#endif
*dst = *src;
dst++;
src++;
cword_val = cword_val >> 1;
}
#if QLZ_COMPRESSION_LEVEL <= 2
update_hash_upto(state, &last_hashed, last_destination_byte - 3); // todo, use constant
#endif
return size;
}
}
}
}
size_t qlz_compress(const void *source, char *destination, size_t size, qlz_state_compress *state)
{
size_t r;
ui32 compressed;
size_t base;
if(size == 0 || size > 0xffffffff - 400)
return 0;
if(size < 216)
base = 3;
else
base = 9;
#if QLZ_STREAMING_BUFFER > 0
if (state->stream_counter + size - 1 >= QLZ_STREAMING_BUFFER)
#endif
{
reset_table_compress(state);
r = base + qlz_compress_core((const unsigned char *)source, (unsigned char*)destination + base, size, state);
#if QLZ_STREAMING_BUFFER > 0
reset_table_compress(state);
#endif
if(r == base)
{
memcpy(destination + base, source, size);
r = size + base;
compressed = 0;
}
else
{
compressed = 1;
}
state->stream_counter = 0;
}
#if QLZ_STREAMING_BUFFER > 0
else
{
unsigned char *src = state->stream_buffer + state->stream_counter;
memcpy(src, source, size);
r = base + qlz_compress_core(src, (unsigned char*)destination + base, size, state);
if(r == base)
{
memcpy(destination + base, src, size);
r = size + base;
compressed = 0;
reset_table_compress(state);
}
else
{
compressed = 1;
}
state->stream_counter += size;
}
#endif
if(base == 3)
{
*destination = (unsigned char)(0 | compressed);
*(destination + 1) = (unsigned char)r;
*(destination + 2) = (unsigned char)size;
}
else
{
*destination = (unsigned char)(2 | compressed);
fast_write((ui32)r, destination + 1, 4);
fast_write((ui32)size, destination + 5, 4);
}
*destination |= (QLZ_COMPRESSION_LEVEL << 2);
*destination |= (1 << 6);
*destination |= ((QLZ_STREAMING_BUFFER == 0 ? 0 : (QLZ_STREAMING_BUFFER == 100000 ? 1 : (QLZ_STREAMING_BUFFER == 1000000 ? 2 : 3))) << 4);
// 76543210
// 01SSLLHC
return r;
}
size_t qlz_decompress(const char *source, void *destination, qlz_state_decompress *state)
{
size_t dsiz = qlz_size_decompressed(source);
#if QLZ_STREAMING_BUFFER > 0
if (state->stream_counter + qlz_size_decompressed(source) - 1 >= QLZ_STREAMING_BUFFER)
#endif
{
if((*source & 1) == 1)
{
reset_table_decompress(state);
dsiz = qlz_decompress_core((const unsigned char *)source, (unsigned char *)destination, dsiz, state, (const unsigned char *)destination);
}
else
{
memcpy(destination, source + qlz_size_header(source), dsiz);
}
state->stream_counter = 0;
reset_table_decompress(state);
}
#if QLZ_STREAMING_BUFFER > 0
else
{
unsigned char *dst = state->stream_buffer + state->stream_counter;
if((*source & 1) == 1)
{
dsiz = qlz_decompress_core((const unsigned char *)source, dst, dsiz, state, (const unsigned char *)state->stream_buffer);
}
else
{
memcpy(dst, source + qlz_size_header(source), dsiz);
reset_table_decompress(state);
}
memcpy(destination, dst, dsiz);
state->stream_counter += dsiz;
}
#endif
return dsiz;
}

150
thirdparty/DBoW3/src/quicklz.h vendored
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@ -1,150 +0,0 @@
#ifndef QLZ_HEADER
#define QLZ_HEADER
// Fast data compression library
// Copyright (C) 2006-2011 Lasse Mikkel Reinhold
// lar@quicklz.com
//
// QuickLZ can be used for free under the GPL 1, 2 or 3 license (where anything
// released into public must be open source) or under a commercial license if such
// has been acquired (see http://www.quicklz.com/order.html). The commercial license
// does not cover derived or ported versions created by third parties under GPL.
// You can edit following user settings. Data must be decompressed with the same
// setting of QLZ_COMPRESSION_LEVEL and QLZ_STREAMING_BUFFER as it was compressed
// (see manual). If QLZ_STREAMING_BUFFER > 0, scratch buffers must be initially
// zeroed out (see manual). First #ifndef makes it possible to define settings from
// the outside like the compiler command line.
// 1.5.0 final
#ifndef QLZ_COMPRESSION_LEVEL
// 1 gives fastest compression speed. 3 gives fastest decompression speed and best
// compression ratio.
#define QLZ_COMPRESSION_LEVEL 1
//#define QLZ_COMPRESSION_LEVEL 2
//#define QLZ_COMPRESSION_LEVEL 3
// If > 0, zero out both states prior to first call to qlz_compress() or qlz_decompress()
// and decompress packets in the same order as they were compressed
#define QLZ_STREAMING_BUFFER 0
//#define QLZ_STREAMING_BUFFER 100000
//#define QLZ_STREAMING_BUFFER 1000000
// Guarantees that decompression of corrupted data cannot crash. Decreases decompression
// speed 10-20%. Compression speed not affected.
//#define QLZ_MEMORY_SAFE
#endif
#define QLZ_VERSION_MAJOR 1
#define QLZ_VERSION_MINOR 5
#define QLZ_VERSION_REVISION 0
// Using size_t, memset() and memcpy()
#include <string.h>
// Verify compression level
#if QLZ_COMPRESSION_LEVEL != 1 && QLZ_COMPRESSION_LEVEL != 2 && QLZ_COMPRESSION_LEVEL != 3
#error QLZ_COMPRESSION_LEVEL must be 1, 2 or 3
#endif
typedef unsigned int ui32;
typedef unsigned short int ui16;
// Decrease QLZ_POINTERS for level 3 to increase compression speed. Do not touch any other values!
#if QLZ_COMPRESSION_LEVEL == 1
#define QLZ_POINTERS 1
#define QLZ_HASH_VALUES 4096
#elif QLZ_COMPRESSION_LEVEL == 2
#define QLZ_POINTERS 4
#define QLZ_HASH_VALUES 2048
#elif QLZ_COMPRESSION_LEVEL == 3
#define QLZ_POINTERS 16
#define QLZ_HASH_VALUES 4096
#endif
// Detect if pointer size is 64-bit. It's not fatal if some 64-bit target is not detected because this is only for adding an optional 64-bit optimization.
#if defined _LP64 || defined __LP64__ || defined __64BIT__ || _ADDR64 || defined _WIN64 || defined __arch64__ || __WORDSIZE == 64 || (defined __sparc && defined __sparcv9) || defined __x86_64 || defined __amd64 || defined __x86_64__ || defined _M_X64 || defined _M_IA64 || defined __ia64 || defined __IA64__
#define QLZ_PTR_64
#endif
// hash entry
typedef struct
{
#if QLZ_COMPRESSION_LEVEL == 1
ui32 cache;
#if defined QLZ_PTR_64 && QLZ_STREAMING_BUFFER == 0
unsigned int offset;
#else
const unsigned char *offset;
#endif
#else
const unsigned char *offset[QLZ_POINTERS];
#endif
} qlz_hash_compress;
typedef struct
{
#if QLZ_COMPRESSION_LEVEL == 1
const unsigned char *offset;
#else
const unsigned char *offset[QLZ_POINTERS];
#endif
} qlz_hash_decompress;
// states
typedef struct
{
#if QLZ_STREAMING_BUFFER > 0
unsigned char stream_buffer[QLZ_STREAMING_BUFFER];
#endif
size_t stream_counter;
qlz_hash_compress hash[QLZ_HASH_VALUES];
unsigned char hash_counter[QLZ_HASH_VALUES];
} qlz_state_compress;
#if QLZ_COMPRESSION_LEVEL == 1 || QLZ_COMPRESSION_LEVEL == 2
typedef struct
{
#if QLZ_STREAMING_BUFFER > 0
unsigned char stream_buffer[QLZ_STREAMING_BUFFER];
#endif
qlz_hash_decompress hash[QLZ_HASH_VALUES];
unsigned char hash_counter[QLZ_HASH_VALUES];
size_t stream_counter;
} qlz_state_decompress;
#elif QLZ_COMPRESSION_LEVEL == 3
typedef struct
{
#if QLZ_STREAMING_BUFFER > 0
unsigned char stream_buffer[QLZ_STREAMING_BUFFER];
#endif
#if QLZ_COMPRESSION_LEVEL <= 2
qlz_hash_decompress hash[QLZ_HASH_VALUES];
#endif
size_t stream_counter;
} qlz_state_decompress;
#endif
#if defined (__cplusplus)
extern "C" {
#endif
// Public functions of QuickLZ
size_t qlz_size_decompressed(const char *source);
size_t qlz_size_compressed(const char *source);
size_t qlz_compress(const void *source, char *destination, size_t size, qlz_state_compress *state);
size_t qlz_decompress(const char *source, void *destination, qlz_state_decompress *state);
int qlz_get_setting(int setting);
#if defined (__cplusplus)
}
#endif
#endif

159
thirdparty/DBoW3/src/timers.h vendored
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@ -1,159 +0,0 @@
#ifndef DBoW3_TIMERS_H
#define DBoW3_TIMERS_H
#include <chrono>
#include <iostream>
#include <string>
#include <vector>
namespace DBoW3 {
// timer
struct ScopeTimer {
std::chrono::high_resolution_clock::time_point begin, end;
std::string name;
bool use;
enum SCALE { NSEC, MSEC, SEC };
SCALE sc;
ScopeTimer(std::string name_, bool use_ = true, SCALE _sc = MSEC) {
name = name_;
use = use_;
sc = _sc;
begin = std::chrono::high_resolution_clock::now();
}
~ScopeTimer() {
if (use) {
end = std::chrono::high_resolution_clock::now();
double fact = 1;
std::string str;
switch (sc) {
case NSEC:
fact = 1;
str = "ns";
break;
case MSEC:
fact = 1e6;
str = "ms";
break;
case SEC:
fact = 1e9;
str = "s";
break;
};
std::cout << "Time (" << name << ")= "
<< double(std::chrono::duration_cast<std::chrono::nanoseconds>(
end - begin)
.count()) /
fact
<< str << std::endl;
;
}
}
};
struct ScopedTimerEvents {
enum SCALE { NSEC, MSEC, SEC };
SCALE sc;
std::vector<std::chrono::high_resolution_clock::time_point> vtimes;
std::vector<std::string> names;
std::string _name;
ScopedTimerEvents(std::string name = "", bool start = true,
SCALE _sc = MSEC) {
if (start) add("start");
sc = _sc;
_name = name;
}
void add(std::string name) {
vtimes.push_back(std::chrono::high_resolution_clock::now());
names.push_back(name);
}
void addspaces(std::vector<std::string> &str) {
// get max size
size_t m = -1;
for (auto &s : str) m = std::max(s.size(), m);
for (auto &s : str) {
while (s.size() < m) s.push_back(' ');
}
}
~ScopedTimerEvents() {
double fact = 1;
std::string str;
switch (sc) {
case NSEC:
fact = 1;
str = "ns";
break;
case MSEC:
fact = 1e6;
str = "ms";
break;
case SEC:
fact = 1e9;
str = "s";
break;
};
add("total");
addspaces(names);
for (size_t i = 1; i < vtimes.size(); i++) {
std::cout << "Time(" << _name << ")-" << names[i] << " "
<< double(std::chrono::duration_cast<std::chrono::nanoseconds>(
vtimes[i] - vtimes[i - 1])
.count()) /
fact
<< str << " "
<< double(std::chrono::duration_cast<std::chrono::nanoseconds>(
vtimes[i] - vtimes[0])
.count()) /
fact
<< str << std::endl;
}
}
};
struct Timer {
enum SCALE { NSEC, MSEC, SEC };
std::chrono::high_resolution_clock::time_point _s;
double sum = 0, n = 0;
std::string _name;
Timer() {}
Timer(std::string name) : _name(name) {}
void setName(std::string name) { _name = name; }
void start() { _s = std::chrono::high_resolution_clock::now(); }
void end() {
auto e = std::chrono::high_resolution_clock::now();
sum += double(
std::chrono::duration_cast<std::chrono::nanoseconds>(e - _s).count());
n++;
}
void print(SCALE sc = MSEC) {
double fact = 1;
std::string str;
switch (sc) {
case NSEC:
fact = 1;
str = "ns";
break;
case MSEC:
fact = 1e6;
str = "ms";
break;
case SEC:
fact = 1e9;
str = "s";
break;
};
std::cout << "Time(" << _name << ")= " << (sum / n) / fact << str
<< std::endl;
}
};
}
#endif