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feature_types.py
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feature_types.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 enum import Enum
import cv2
'''
NOTES:
In order to add a new DETECTOR:
- add a new enum in FeatureDetectorTypes
- manage its 'case' in the detector inialization in feature_manager.py
In order to add a new DESCRIPTOR:
- add a new enum in FeatureDescriptorTypes
- add the related information in the class FeatureInfo below
- manage its 'case' in the descriptor inialization in feature_manager.py
'''
class FeatureDetectorTypes(Enum):
NONE = 0
SHI_TOMASI = 1 # "Good Features To Track"
FAST = 2 # "Faster and better: a machine learning approachto corner detection"
SIFT = 3 # "Object Recognition from Local Scale-Invariant Features"
ROOT_SIFT = 4 # "Three things everyone should know to improve object retrieval"
SURF = 5 # "SURF: Speeded Up Robust Features"
ORB = 6 # "ORB: An efficient alternative to SIFT or SURF"
ORB2 = 7 # interface for ORB-SLAM2 features (ORB + spatial keypoint filtering)
BRISK = 8 # "BRISK: Binary Robust Invariant Scalable Keypoints"
KAZE = 9 # "KAZE Features"
AKAZE = 10 # "Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces"
SUPERPOINT = 11 # [end-to-end] joint detector-descriptor - "SuperPoint: Self-Supervised Interest Point Detection and Description"
AGAST = 12 # "AGAST Corner Detector: faster than FAST and even FAST-ER"
GFTT = 13 # "Good Features To Track" (it includes SHI-TOMASI and HARRIS methods)
MSER = 14 # "Robust Wide Baseline Stereo from Maximally Stable Extremal Regions"
MSD = 15 # "Interest points via maximal self-dissimilarities" - Unfortunately it does not work on my setup!
STAR = 16 # StarDetector - "Censure: Center surround extremas for realtime feature detection and matching"
HL = 17 # Harris-Laplace - "Scale & affine invariant interest point detectors"
D2NET = 18 # [end-to-end] joint detector-descriptor - "D2-Net: A Trainable CNN for Joint Detection and Description of Local Features".
DELF = 19 # [end-to-end] joint detector-descriptor - "Large-Scale Image Retrieval with Attentive Deep Local Features".
CONTEXTDESC = 20 # [end-to-end] only with CONTEXTDESC descriptor - "ContextDesc: Local Descriptor Augmentation with Cross-Modality Context"
LFNET = 21 # [end-to-end] joint detector-descriptor - "LF-Net: Learning Local Features from Images"
R2D2 = 22 # [end-to-end] joint detector-descriptor - "R2D2: Repeatable and Reliable Detector and Descriptor"
KEYNET = 23 # "Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters"
DISK = 24 # [end-to-end] joint detector-descriptor - "DISK: Learning local features with policy gradient"
class FeatureDescriptorTypes(Enum):
NONE = 0 # used for LK tracker (in main_vo.py)
SIFT = 1 # "Object Recognition from Local Scale-Invariant Features"
ROOT_SIFT = 2 # "Three things everyone should know to improve object retrieval"
SURF = 3 # "SURF: Speeded Up Robust Features"
ORB = 4 # [binary] "ORB: An efficient alternative to SIFT or SURF"
ORB2 = 5 # [binary] interface for ORBSLAM2 features
BRISK = 6 # [binary] "BRISK: Binary Robust Invariant Scalable Keypoints"
KAZE = 7 # only with KAZE or AKAZE detectors - "KAZE Features"
AKAZE = 8 # [binary] only with KAZE or AKAZE detectors - "Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces"
FREAK = 9 # [binary] only descriptor - "FREAK: Fast retina keypoint"
SUPERPOINT = 10 # [end-to-end] only with SUPERPOINT detector - "SuperPoint: Self-Supervised Interest Point Detection and Description"
TFEAT = 11 # only descriptor - " Learning local feature descriptors with triplets and shallow convolutional neural networks"
BOOST_DESC = 12 # [binary] only descriptor - "Learning Image Descriptors with Boosting"
DAISY = 13 # only descriptor - "Daisy: An efficient dense descriptor applied to wide baseline stereo"
LATCH = 14 # [binary] only descriptor - "LATCH: Learned Arrangements of Three Patch Codes"
LUCID = 15 # [binary] only descriptor - "Locally uniform comparison image descriptor" - (it requires a color image) <-- !N.B.: not producing good results!
VGG = 16 # only descriptor - "Learning local feature descriptors using convex optimisation"
HARDNET = 17 # only descriptor - "Working hard to know your neighbor’s margins: Local descriptor learning loss"
GEODESC = 18 # only descriptor - "GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints"
SOSNET = 19 # only descriptor - "SOSNet:Second Order Similarity Regularization for Local Descriptor Learning"
L2NET = 20 # only descriptor - "L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space"
LOGPOLAR = 21 # only descriptor - "Beyond Cartesian Representations for Local Descriptors"
D2NET = 22 # [end-to-end] joint detector-descriptor (only with D2NET detector) - "D2-Net: A Trainable CNN for Joint Detection and Description of Local Features".
DELF = 23 # [end-to-end] joint detector-descriptor (only with DELF detector) - "Large-Scale Image Retrieval with Attentive Deep Local Features".
CONTEXTDESC = 24 # [end-to-end] only with CONTEXTDESC detector - "ContextDesc: Local Descriptor Augmentation with Cross-Modality Context"
LFNET = 25 # [end-to-end] joint detector-descriptor (only with LFNET detector) - "LF-Net: Learning Local Features from Images"
R2D2 = 26 # [end-to-end] joint detector-descriptor (only with R2D2 detector) - "R2D2: Repeatable and Reliable Detector and Descriptor"
KEYNET = 27 # keynet descriptor is HARDNET (only with KEYNET detector) - "Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters"
BEBLID = 28 # [binary] only descriptor - " BEBLID: Boosted Efficient Binary Local Image Descriptor"
DISK = 29 # [end-to-end] joint detector-descriptor - "DISK: Learning local features with policy gradient"
class FeatureInfo(object):
norm_type = dict()
max_descriptor_distance = dict() # initial reference max descriptor distances used by SLAM for locally searching matches around frame keypoints;
# these are initialized and then updated by using standard deviation robust estimation (MAD) and exponential smoothing
# N.B.: these intial reference distances can be easily estimated by using test_feature_matching.py
# where (3 x sigma_mad) is computed
#
norm_type[FeatureDescriptorTypes.NONE] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.NONE] = float('inf')
#
norm_type[FeatureDescriptorTypes.SIFT] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.SIFT] = 450 # SIFT
#
norm_type[FeatureDescriptorTypes.ROOT_SIFT] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.ROOT_SIFT] = 0.9 # ROOT_SIFT
#
norm_type[FeatureDescriptorTypes.SURF] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.SURF] = 0.45 # SURF
#
norm_type[FeatureDescriptorTypes.ORB] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.ORB] = 100 # ORB
#
norm_type[FeatureDescriptorTypes.ORB2] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.ORB2] = 100 # ORB
#
norm_type[FeatureDescriptorTypes.BRISK] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.BRISK] = 200 # BRISK
#
norm_type[FeatureDescriptorTypes.KAZE] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.KAZE] = 1.2 # KAZE
#
norm_type[FeatureDescriptorTypes.AKAZE] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.AKAZE] = 190 # AKAZE
#
norm_type[FeatureDescriptorTypes.FREAK] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.FREAK] = 180 # FREAK
#
norm_type[FeatureDescriptorTypes.SUPERPOINT] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.SUPERPOINT] = 1.30 # SUPERPOINT
#
norm_type[FeatureDescriptorTypes.TFEAT] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.TFEAT] = 11 # TFEAT
#
norm_type[FeatureDescriptorTypes.BOOST_DESC] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.BOOST_DESC] = 100 # BOOST_DESC
#
norm_type[FeatureDescriptorTypes.DAISY] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.DAISY] = 0.4 # DAISY
#
norm_type[FeatureDescriptorTypes.LATCH] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.LATCH] = 120 # LATCH
#
norm_type[FeatureDescriptorTypes.LUCID] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.LUCID] = 100 # LUCID
#
norm_type[FeatureDescriptorTypes.VGG] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.VGG] = 5 # VGG
#
norm_type[FeatureDescriptorTypes.HARDNET] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.HARDNET] = 1.8 # HARDNET
#
norm_type[FeatureDescriptorTypes.GEODESC] = cv2.NORM_L2 # unless GeodescFeature2D.quantize == True
max_descriptor_distance[FeatureDescriptorTypes.GEODESC] = 0.4 # GEODESC
#
norm_type[FeatureDescriptorTypes.SOSNET] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.SOSNET] = 2 # SOSNET
#
norm_type[FeatureDescriptorTypes.L2NET] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.L2NET] = 2.9 # L2NET
#
norm_type[FeatureDescriptorTypes.LOGPOLAR] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.LOGPOLAR] = 3.2 # LOGPOLAR
#
norm_type[FeatureDescriptorTypes.D2NET] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.D2NET] = 2.8 # D2NET
#
norm_type[FeatureDescriptorTypes.DELF] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.DELF] = 2.1 # DELF
#
norm_type[FeatureDescriptorTypes.CONTEXTDESC] = cv2.NORM_L2 # unless ContextDescFeature2D.quantize == True
max_descriptor_distance[FeatureDescriptorTypes.CONTEXTDESC] = 1.6 # CONTEXTDESC
#
norm_type[FeatureDescriptorTypes.LFNET] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.LFNET] = 2.2 # LFNET
#
norm_type[FeatureDescriptorTypes.R2D2] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.R2D2] = 1.4 # R2D2
#
norm_type[FeatureDescriptorTypes.KEYNET] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.KEYNET] = 1.6 # KEYNET
#
norm_type[FeatureDescriptorTypes.BEBLID] = cv2.NORM_HAMMING
max_descriptor_distance[FeatureDescriptorTypes.BEBLID] = 110 # BEBLID (BEBLID_SIZE_256_BITS)
#
norm_type[FeatureDescriptorTypes.DISK] = cv2.NORM_L2
max_descriptor_distance[FeatureDescriptorTypes.DISK] = 3.1 # DISK