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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 feature_matcher_factory, FeatureMatcherTypes
from parameters import Parameters
# some default parameters
kNumFeatures=Parameters.kNumFeatures
kRatioTest=Parameters.kFeatureMatchRatioTest
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(object):
# 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
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.ORB2,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
# =====================================
# LK trackers (these can only be used with VisualOdometry() ... at the present time)
LK_SHI_TOMASI = dict(num_features=kNumFeatures,
num_levels = 3,
detector_type = FeatureDetectorTypes.SHI_TOMASI,
descriptor_type = FeatureDescriptorTypes.NONE,
tracker_type = FeatureTrackerTypes.LK)
LK_FAST = dict(num_features=kNumFeatures,
num_levels = 3,
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.NONE,
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,
match_ratio_test = kRatioTest,
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,
match_ratio_test = kRatioTest,
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,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
FAST_FREAK = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.FREAK,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
BRISK = dict(num_features=kNumFeatures,
num_levels = 4,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.BRISK,
descriptor_type = FeatureDescriptorTypes.BRISK,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
BRISK_TFEAT = dict(num_features=kNumFeatures,
num_levels = 4,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.BRISK,
descriptor_type = FeatureDescriptorTypes.TFEAT,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
ORB = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB,
descriptor_type = FeatureDescriptorTypes.ORB,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
ORB2 = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.ORB2,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
BRISK = dict(num_features=kNumFeatures,
num_levels = 8,
detector_type = FeatureDetectorTypes.BRISK,
descriptor_type = FeatureDescriptorTypes.BRISK,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
KAZE = dict(num_features=kNumFeatures,
num_levels = 8,
detector_type = FeatureDetectorTypes.KAZE,
descriptor_type = FeatureDescriptorTypes.KAZE,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
AKAZE = dict(num_features=kNumFeatures,
num_levels = 8,
detector_type = FeatureDetectorTypes.AKAZE,
descriptor_type = FeatureDescriptorTypes.AKAZE,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
SIFT = dict(num_features=kNumFeatures,
detector_type = FeatureDetectorTypes.SIFT,
descriptor_type = FeatureDescriptorTypes.SIFT,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
ROOT_SIFT = dict(num_features=kNumFeatures,
detector_type = FeatureDetectorTypes.ROOT_SIFT,
descriptor_type = FeatureDescriptorTypes.ROOT_SIFT,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
SIFT_HARDNET = dict(num_features=kNumFeatures,
detector_type = FeatureDetectorTypes.SIFT,
descriptor_type = FeatureDescriptorTypes.HARDNET,
match_ratio_test = kRatioTest,
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,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
SURF_SIFT = dict(num_features=kNumFeatures,
num_levels = 8,
detector_type = FeatureDetectorTypes.SURF,
descriptor_type = FeatureDescriptorTypes.ROOT_SIFT,
match_ratio_test = kRatioTest,
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,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
CONTEXTDESC = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.CONTEXTDESC,
descriptor_type = FeatureDescriptorTypes.CONTEXTDESC,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
ASLFEAT = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ASLFEAT,
descriptor_type = FeatureDescriptorTypes.ASLFEAT,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
R2D2 = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.R2D2,
descriptor_type = FeatureDescriptorTypes.R2D2,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
KEYNET = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.KEYNET,
descriptor_type = FeatureDescriptorTypes.KEYNET,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
DISK = dict(num_features=kNumFeatures,
num_levels = 1,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.DISK,
descriptor_type = FeatureDescriptorTypes.DISK,
match_ratio_test = kRatioTest,
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,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
ORB2_BEBLID = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.BEBLID,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
ORB2_HARDNET = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.HARDNET,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
ORB2_SOSNET = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.SOSNET,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)
ORB2_L2NET = dict(num_features=kNumFeatures,
num_levels = 8,
scale_factor = 1.2,
detector_type = FeatureDetectorTypes.ORB2,
descriptor_type = FeatureDescriptorTypes.L2NET,
match_ratio_test = kRatioTest,
tracker_type = kTrackerType)