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feature_tracker.py
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feature_tracker.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/>.
"""
import numpy as np
import cv2
from enum import Enum
from feature_manager import feature_manager_factory
from feature_types import FeatureDetectorTypes, FeatureDescriptorTypes, FeatureInfo
from feature_matcher import feature_matcher_factory, FeatureMatcherTypes
from utils_sys import Printer, import_from
from utils_geom import hamming_distance, hamming_distances, l2_distance, l2_distances
from parameters import Parameters
kMinNumFeatureDefault = 2000
kLkPyrOpticFlowNumLevelsMin = 3 # maximal pyramid level number for LK optic flow
kRatioTest = Parameters.kFeatureMatchRatioTest
class FeatureTrackerTypes(Enum):
LK = 0 # Lucas Kanade pyramid optic flow (use pixel patch as "descriptor" and matching by optimization)
DES_BF = 1 # descriptor-based, brute force matching with knn
DES_FLANN = 2 # descriptor-based, FLANN-based matching
def feature_tracker_factory(num_features=kMinNumFeatureDefault,
num_levels = 1, # number of pyramid levels or octaves for detector and descriptor
scale_factor = 1.2, # detection scale factor (if it can be set, otherwise it is automatically computed)
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.ORB,
match_ratio_test = kRatioTest,
tracker_type = FeatureTrackerTypes.LK):
if tracker_type == FeatureTrackerTypes.LK:
return LkFeatureTracker(num_features=num_features,
num_levels = num_levels,
scale_factor = scale_factor,
detector_type = detector_type,
descriptor_type = descriptor_type,
match_ratio_test = match_ratio_test,
tracker_type = tracker_type)
else:
return DescriptorFeatureTracker(num_features=num_features,
num_levels = num_levels,
scale_factor = scale_factor,
detector_type = detector_type,
descriptor_type = descriptor_type,
match_ratio_test = match_ratio_test,
tracker_type = tracker_type)
return None
class FeatureTrackingResult(object):
def __init__(self):
self.kps_ref = None # all reference keypoints (numpy array Nx2)
self.kps_cur = None # all current keypoints (numpy array Nx2)
self.des_cur = None # all current descriptors (numpy array NxD)
self.idxs_ref = None # indexes of matches in kps_ref so that kps_ref_matched = kps_ref[idxs_ref] (numpy array of indexes)
self.idxs_cur = None # indexes of matches in kps_cur so that kps_cur_matched = kps_cur[idxs_cur] (numpy array of indexes)
self.kps_ref_matched = None # reference matched keypoints, kps_ref_matched = kps_ref[idxs_ref]
self.kps_cur_matched = None # current matched keypoints, kps_cur_matched = kps_cur[idxs_cur]
# Base class for a feature tracker.
# It mainly contains a feature manager and a feature matcher.
class FeatureTracker(object):
def __init__(self, num_features=kMinNumFeatureDefault,
num_levels = 1, # number of pyramid levels for detector and descriptor
scale_factor = 1.2, # detection scale factor (if it can be set, otherwise it is automatically computed)
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.ORB,
match_ratio_test = kRatioTest,
tracker_type = FeatureTrackerTypes.LK):
self.detector_type = detector_type
self.descriptor_type = descriptor_type
self.tracker_type = tracker_type
self.feature_manager = None # it contains both detector and descriptor
self.matcher = None # it contain descriptors matching methods based on BF, FLANN, etc.
@property
def num_features(self):
return self.feature_manager.num_features
@property
def num_levels(self):
return self.feature_manager.num_levels
@property
def scale_factor(self):
return self.feature_manager.scale_factor
@property
def norm_type(self):
return self.feature_manager.norm_type
@property
def descriptor_distance(self):
return self.feature_manager.descriptor_distance
@property
def descriptor_distances(self):
return self.feature_manager.descriptor_distances
# out: keypoints and descriptors
def detectAndCompute(self, frame, mask):
return None, None
# out: FeatureTrackingResult()
def track(self, image_ref, image_cur, kps_ref, des_ref):
return FeatureTrackingResult()
# Lucas-Kanade Tracker: it uses raw pixel patches as "descriptors" and track/"match" by using Lucas Kanade pyr optic flow
class LkFeatureTracker(FeatureTracker):
def __init__(self, num_features=kMinNumFeatureDefault,
num_levels = 3, # number of pyramid levels for detector
scale_factor = 1.2, # detection scale factor (if it can be set, otherwise it is automatically computed)
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.NONE,
match_ratio_test = kRatioTest,
tracker_type = FeatureTrackerTypes.LK):
super().__init__(num_features=num_features,
num_levels=num_levels,
scale_factor=scale_factor,
detector_type=detector_type,
descriptor_type=descriptor_type,
tracker_type=tracker_type)
self.feature_manager = feature_manager_factory(num_features=num_features,
num_levels=num_levels,
scale_factor=scale_factor,
detector_type=detector_type,
descriptor_type=descriptor_type)
#if num_levels < 3:
# Printer.green('LkFeatureTracker: forcing at least 3 levels on LK pyr optic flow')
# num_levels = 3
optic_flow_num_levels = max(kLkPyrOpticFlowNumLevelsMin,num_levels)
Printer.green('LkFeatureTracker: num levels on LK pyr optic flow: ', optic_flow_num_levels)
# we use LK pyr optic flow for matching
self.lk_params = dict(winSize = (21, 21),
maxLevel = optic_flow_num_levels,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 30, 0.01))
# out: keypoints and empty descriptors
def detectAndCompute(self, frame, mask=None):
return self.feature_manager.detect(frame, mask), None
# out: FeatureTrackingResult()
def track(self, image_ref, image_cur, kps_ref, des_ref = None):
kps_cur, st, err = cv2.calcOpticalFlowPyrLK(image_ref, image_cur, kps_ref, None, **self.lk_params) #shape: [k,2] [k,1] [k,1]
st = st.reshape(st.shape[0])
res = FeatureTrackingResult()
#res.idxs_ref = (st == 1)
res.idxs_ref = [i for i,v in enumerate(st) if v== 1]
res.idxs_cur = res.idxs_ref.copy()
res.kps_ref_matched = kps_ref[res.idxs_ref]
res.kps_cur_matched = kps_cur[res.idxs_cur]
res.kps_ref = res.kps_ref_matched # with LK we follow feature trails hence we can forget unmatched features
res.kps_cur = res.kps_cur_matched
res.des_cur = None
return res
# Extract features by using desired detector and descriptor, match keypoints by using desired matcher on computed descriptors
class DescriptorFeatureTracker(FeatureTracker):
def __init__(self, num_features=kMinNumFeatureDefault,
num_levels = 1, # number of pyramid levels for detector
scale_factor = 1.2, # detection scale factor (if it can be set, otherwise it is automatically computed)
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.ORB,
match_ratio_test = kRatioTest,
tracker_type = FeatureTrackerTypes.DES_FLANN):
super().__init__(num_features=num_features,
num_levels=num_levels,
scale_factor=scale_factor,
detector_type=detector_type,
descriptor_type=descriptor_type,
match_ratio_test = match_ratio_test,
tracker_type=tracker_type)
self.feature_manager = feature_manager_factory(num_features=num_features,
num_levels=num_levels,
scale_factor=scale_factor,
detector_type=detector_type,
descriptor_type=descriptor_type)
if tracker_type == FeatureTrackerTypes.DES_FLANN:
self.matching_algo = FeatureMatcherTypes.FLANN
elif tracker_type == FeatureTrackerTypes.DES_BF:
self.matching_algo = FeatureMatcherTypes.BF
else:
raise ValueError("Unmanaged matching algo for feature tracker %s" % self.tracker_type)
# init matcher
self.matcher = feature_matcher_factory(norm_type=self.norm_type, ratio_test=match_ratio_test, type=self.matching_algo)
# out: keypoints and descriptors
def detectAndCompute(self, frame, mask=None):
return self.feature_manager.detectAndCompute(frame, mask)
# out: FeatureTrackingResult()
def track(self, image_ref, image_cur, kps_ref, des_ref):
kps_cur, des_cur = self.detectAndCompute(image_cur)
# convert from list of keypoints to an array of points
kps_cur = np.array([x.pt for x in kps_cur], dtype=np.float32)
idxs_ref, idxs_cur = self.matcher.match(des_ref, des_cur) #knnMatch(queryDescriptors,trainDescriptors)
#print('num matches: ', len(matches))
res = FeatureTrackingResult()
res.kps_ref = kps_ref # all the reference keypoints
res.kps_cur = kps_cur # all the current keypoints
res.des_cur = des_cur # all the current descriptors
res.kps_ref_matched = np.asarray(kps_ref[idxs_ref]) # the matched ref kps
res.idxs_ref = np.asarray(idxs_ref)
res.kps_cur_matched = np.asarray(kps_cur[idxs_cur]) # the matched cur kps
res.idxs_cur = np.asarray(idxs_cur)
return res