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feature_tracker_configs.py
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"""
* This file is part of PYSLAM
*
* Copyright (C) 2016-present Luigi Freda <luigi dot freda at gmail dot com>
*
* PYSLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* PYSLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with PYSLAM. If not, see <http://www.gnu.org/licenses/>.
"""
from feature_tracker import feature_tracker_factory, FeatureTrackerTypes
from feature_manager import feature_manager_factory
from feature_types import FeatureDetectorTypes, FeatureDescriptorTypes, FeatureInfo
from feature_matcher import FeatureMatcherTypes
from config_parameters import Parameters
from utils_sys import Printer
# some default parameters
kNumFeatures=Parameters.kNumFeatures
kDefaultRatioTest=Parameters.kFeatureMatchDefaultRatioTest
kTrackerType = FeatureTrackerTypes.DES_BF # default descriptor-based, brute force matching with knn
#kTrackerType = FeatureTrackerTypes.DES_FLANN # default descriptor-based, FLANN-based matching
"""
A collection of ready-to-used feature tracker configurations
"""
class FeatureTrackerConfigs:
@staticmethod
def get_config_from_name(config_name):
config_dict = getattr(FeatureTrackerConfigs, config_name, None)
if config_dict is not None:
Printer.cyan("FeatureTrackerConfigs: Configuration loaded:", config_dict)
else:
Printer.red(f"FeatureTrackerConfigs: No configuration found for '{config_name}'")
return config_dict
# Test/Template configuration: you can use this to quickly test
# - your custom parameters and
# - favourite descriptor and detector (check the file feature_types.py)
TEST = dict(num_features=kNumFeatures,
num_levels = 8, # N.B: some detectors/descriptors do not allow to set num_levels or they set it on their own
scale_factor = 1.2, # N.B: some detectors/descriptors do not allow to set scale_factor or they set it on their own
sigma_level0 = Parameters.kSigmaLevel0,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.ORB2,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
# =====================================
# LK trackers (these can only be used with VisualOdometryEducational() ... at the present time)
LK_SHI_TOMASI = dict(num_features=kNumFeatures,
num_levels = 3,
detector_type = FeatureDetectorTypes.SHI_TOMASI,
descriptor_type = FeatureDescriptorTypes.NONE,
sigma_level0 = Parameters.kSigmaLevel0,
tracker_type = FeatureTrackerTypes.LK)
LK_FAST = dict(num_features=kNumFeatures,
num_levels = 3,
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.NONE,
sigma_level0 = Parameters.kSigmaLevel0,
tracker_type = FeatureTrackerTypes.LK)
# =====================================
# Descriptor-based 'trackers'
SHI_TOMASI_ORB = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.SHI_TOMASI,
descriptor_type = FeatureDescriptorTypes.ORB,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
SHI_TOMASI_FREAK = dict(num_features=kNumFeatures,
num_levels=8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.SHI_TOMASI,
descriptor_type = FeatureDescriptorTypes.FREAK,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
FAST_ORB = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.ORB,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
FAST_FREAK = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.FREAK,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
BRISK = dict(num_features=kNumFeatures,
num_levels = 4,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.BRISK,
descriptor_type = FeatureDescriptorTypes.BRISK,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
BRISK_TFEAT = dict(num_features=kNumFeatures,
num_levels = 4,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.BRISK,
descriptor_type = FeatureDescriptorTypes.TFEAT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
ORB = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB,
descriptor_type = FeatureDescriptorTypes.ORB,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
ORB2 = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.ORB2,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
BRISK = dict(num_features=kNumFeatures,
num_levels = 8,
detector_type = FeatureDetectorTypes.BRISK,
descriptor_type = FeatureDescriptorTypes.BRISK,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
KAZE = dict(num_features=kNumFeatures,
num_levels = 8,
detector_type = FeatureDetectorTypes.KAZE,
descriptor_type = FeatureDescriptorTypes.KAZE,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
AKAZE = dict(num_features=kNumFeatures,
num_levels = 8,
detector_type = FeatureDetectorTypes.AKAZE,
descriptor_type = FeatureDescriptorTypes.AKAZE,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
SIFT = dict(num_features=kNumFeatures, # independently computes the number of octaves
detector_type = FeatureDetectorTypes.SIFT,
descriptor_type = FeatureDescriptorTypes.SIFT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
ROOT_SIFT = dict(num_features=kNumFeatures, # independently computes the number of octaves as SIFT
detector_type = FeatureDetectorTypes.ROOT_SIFT,
descriptor_type = FeatureDescriptorTypes.ROOT_SIFT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
# NOTE: SURF is a patented algorithm and not included in the new opencv versions
# If you want to test it, you can install and old version of opencv that supports it: run
# $ pip3 uninstall opencv-contrib-python
# $ pip3 install opencv-contrib-python==3.4.2.16
SURF = dict(num_features=kNumFeatures,
num_levels = 8,
detector_type = FeatureDetectorTypes.SURF,
descriptor_type = FeatureDescriptorTypes.SURF,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
SUPERPOINT = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.SUPERPOINT,
descriptor_type = FeatureDescriptorTypes.SUPERPOINT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = 0.9,
tracker_type = kTrackerType)
XFEAT = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.XFEAT,
descriptor_type = FeatureDescriptorTypes.XFEAT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = 0.8,
tracker_type = kTrackerType)
XFEAT_XFEAT = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.XFEAT,
descriptor_type = FeatureDescriptorTypes.XFEAT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = 0.8,
tracker_type = FeatureTrackerTypes.XFEAT) # <= Using XFEAT matcher here!
XFEAT_LIGHTGLUE = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.XFEAT,
descriptor_type = FeatureDescriptorTypes.XFEAT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = 0.8,
tracker_type = FeatureTrackerTypes.XFEAT,
other_data_dict=dict(submatcher_type = 'lightglue')) # <= Using XFEAT custom LIGHTGLUE matcher here!
LIGHTGLUE = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.SUPERPOINT,
descriptor_type = FeatureDescriptorTypes.SUPERPOINT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = 1.0,
tracker_type = FeatureTrackerTypes.LIGHTGLUE)
LIGHTGLUE_DISK = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.DISK,
descriptor_type = FeatureDescriptorTypes.DISK,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = 1.0,
tracker_type = FeatureTrackerTypes.LIGHTGLUE)
LIGHTGLUE_ALIKED = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ALIKED,
descriptor_type = FeatureDescriptorTypes.ALIKED,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = 1.0,
tracker_type = FeatureTrackerTypes.LIGHTGLUE)
LIGHTGLUESIFT = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.LIGHTGLUESIFT,
descriptor_type = FeatureDescriptorTypes.LIGHTGLUESIFT,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = 1.0,
tracker_type = FeatureTrackerTypes.LIGHTGLUE)
DELF = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.DELF,
descriptor_type = FeatureDescriptorTypes.DELF,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
D2NET = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.D2NET,
descriptor_type = FeatureDescriptorTypes.D2NET,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
R2D2 = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.R2D2,
descriptor_type = FeatureDescriptorTypes.R2D2,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
LFNET = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.LFNET,
descriptor_type = FeatureDescriptorTypes.LFNET,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
CONTEXTDESC = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.CONTEXTDESC,
descriptor_type = FeatureDescriptorTypes.CONTEXTDESC,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
KEYNET = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.KEYNET,
descriptor_type = FeatureDescriptorTypes.KEYNET,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
DISK = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.DISK,
descriptor_type = FeatureDescriptorTypes.DISK,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
ALIKED = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ALIKED,
descriptor_type = FeatureDescriptorTypes.ALIKED,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
KEYNETAFFNETHARDNET = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.KEYNETAFFNETHARDNET,
descriptor_type = FeatureDescriptorTypes.KEYNETAFFNETHARDNET,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
# =====================================
# Descriptor-based 'trackers' with ORB2
ORB2_FREAK = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.FREAK,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
ORB2_BEBLID = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.BEBLID,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
ORB2_HARDNET = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.HARDNET,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
ORB2_SOSNET = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.SOSNET,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
ORB2_L2NET = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.L2NET,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = kTrackerType)
# NOTE: The extraction of independent descriptors from a single image does not make sense for the MASt3R/DUST3R model.
# The model ground the image matching in a 3D context defined by two images of the same place.
# MAST3R = dict(num_features=kNumFeatures,
# num_levels = 1,
# scale_factor = 1.2,
# detector_type = FeatureDetectorTypes.FAST,
# descriptor_type = FeatureDescriptorTypes.MAST3R,
# sigma_level0 = Parameters.kSigmaLevel0,
# match_ratio_test = 0.8,
# tracker_type = kTrackerType)
# =====================================
# Matcher-based 'trackers' (Detector-Free)
# Note: The following matchers are NOT able to extract keypoints and descriptors on a single provided image. They work directly on a pair of images (img1, img2) and produce
# as a result a pair of corresponding keypoint vectors (kps1, kps2).
# By design, if we feed these matchers with video images then the extracted keypoints are different on each image. That is, given:
# - matcher(img1, img2) -> (kps1, kps2a)
# - matcher(img2, img3) -> (kps2b, kps3)
# we have that the keypoint kps2a[i], extrated on img2 the first time, does not necessarily correspond to kps2b[i] or to any other kps2b[j] extracted the second time on img2.
# WARNING: For the reasons explained above, at present, we cannot use these "pure" matchers with classic SLAM architecture. In fact, mapping and localization processes need more than two observations
# for each triangulated 3D point along different frames to obtain persistent map points and properly constrain camera pose optimizations in the Sim(3) manifold.
# An explicit additional mechanism to associate keypoints across images is needed. This is an experimental WIP.
LOFTR = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.NONE,
descriptor_type = FeatureDescriptorTypes.NONE,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = FeatureTrackerTypes.LOFTR)
MAST3R_MATCHER = dict(num_features=kNumFeatures, # N.B.: here, keypoints are not oriented! (i.e. keypoint.angle=0 always)
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.NONE,
descriptor_type = FeatureDescriptorTypes.NONE,
sigma_level0 = Parameters.kSigmaLevel0,
match_ratio_test = kDefaultRatioTest,
tracker_type = FeatureTrackerTypes.MAST3R)