forked from luigifreda/pyslam
-
Notifications
You must be signed in to change notification settings - Fork 0
/
initializer.py
201 lines (160 loc) · 9.36 KB
/
initializer.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
"""
* 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 time
import cv2
from enum import Enum
from frame import Frame, match_frames
from keyframe import KeyFrame
from collections import deque
from map_point import MapPoint
from map import Map
from utils_geom import triangulate_points, triangulate_normalized_points, add_ones, poseRt, inv_T
from camera import Camera, PinholeCamera
from utils_sys import Printer
from parameters import Parameters
kVerbose=True
kRansacThresholdNormalized = 0.0003 # metric threshold used for normalized image coordinates
kRansacProb = 0.999
kMaxIdDistBetweenIntializingFrames = 5 # N.B.: worse performances with values smaller than 5!
kFeatureMatchRatioTestInitializer = Parameters.kFeatureMatchRatioTestInitializer
kNumOfFailuresAfterWichNumMinTriangulatedPointsIsHalved = 10
kMaxLenFrameDeque = 20
class InitializerOutput(object):
def __init__(self):
self.pts = None # 3d points [Nx3]
self.kf_cur = None
self.kf_ref = None
self.idxs_cur = None
self.idxs_ref = None
class Initializer(object):
def __init__(self):
self.mask_match = None
self.mask_recover = None
self.frames = deque(maxlen=kMaxLenFrameDeque) # deque with max length, it is thread-safe
self.idx_f_ref = 0 # index of the reference frame in self.frames buffer
self.f_ref = None
self.num_min_features = Parameters.kInitializerNumMinFeatures
self.num_min_triangulated_points = Parameters.kInitializerNumMinTriangulatedPoints
self.num_failures = 0
def reset(self):
self.frames.clear()
self.f_ref = None
# fit essential matrix E with RANSAC such that: p2.T * E * p1 = 0 where E = [t21]x * R21
# out: Trc homogeneous transformation matrix with respect to 'ref' frame, pr_= Trc * pc_
# N.B.1: trc is estimated up to scale (i.e. the algorithm always returns ||trc||=1, we need a scale in order to recover a translation which is coherent with previous estimated poses)
# N.B.2: this function has problems in the following cases: [see Hartley/Zisserman Book]
# - 'geometrical degenerate correspondences', e.g. all the observed features lie on a plane (the correct model for the correspondences is an homography) or lie a ruled quadric
# - degenerate motions such a pure rotation (a sufficient parallax is required) or anum_edges viewpoint change (where the translation is almost zero)
# N.B.3: the five-point algorithm (used for estimating the Essential Matrix) seems to work well in the degenerate planar cases [Five-Point Motion Estimation Made Easy, Hartley]
# N.B.4: as reported above, in case of pure rotation, this algorithm will compute a useless fundamental matrix which cannot be decomposed to return the rotation
# N.B.5: the OpenCV findEssentialMat function uses the five-point algorithm solver by D. Nister => hence it should work well in the degenerate planar cases
def estimatePose(self, kpn_ref, kpn_cur):
# here, the essential matrix algorithm uses the five-point algorithm solver by D. Nister (see the notes and paper above )
E, self.mask_match = cv2.findEssentialMat(kpn_cur, kpn_ref, focal=1, pp=(0., 0.), method=cv2.RANSAC, prob=kRansacProb, threshold=kRansacThresholdNormalized)
_, R, t, mask = cv2.recoverPose(E, kpn_cur, kpn_ref, focal=1, pp=(0., 0.))
return poseRt(R,t.T) # Trc homogeneous transformation matrix with respect to 'ref' frame, pr_= Trc * pc_
# push the first image
def init(self, f_cur):
self.frames.append(f_cur)
self.f_ref = f_cur
# actually initialize having two available images
def initialize(self, f_cur, img_cur, segmentation=None):
if self.num_failures > kNumOfFailuresAfterWichNumMinTriangulatedPointsIsHalved:
self.num_min_triangulated_points = 0.5 * Parameters.kInitializerNumMinTriangulatedPoints
self.num_failures = 0
Printer.orange('Initializer: halved min num triangulated features to ', self.num_min_triangulated_points)
# prepare the output
out = InitializerOutput()
is_ok = False
#print('num frames: ', len(self.frames))
# if too many frames have passed, move the current idx_f_ref forward
# this is just one very simple policy that can be used
if self.f_ref is not None:
if f_cur.id - self.f_ref.id > kMaxIdDistBetweenIntializingFrames:
self.f_ref = self.frames[-1] # take last frame in the buffer
#self.idx_f_ref = len(self.frames)-1 # take last frame in the buffer
#self.idx_f_ref = self.frames.index(self.f_ref) # since we are using a deque, the code of the previous commented line is not valid anymore
#print('*** idx_f_ref:',self.idx_f_ref)
#self.f_ref = self.frames[self.idx_f_ref]
f_ref = self.f_ref
#print('ref fid: ',self.f_ref.id,', curr fid: ', f_cur.id, ', idxs_ref: ', self.idxs_ref)
# append current frame
self.frames.append(f_cur)
# if the current frames do no have enough features exit
if len(f_ref.kps) < self.num_min_features or len(f_cur.kps) < self.num_min_features:
Printer.red('Inializer: ko - not enough features!')
self.num_failures += 1
return out, is_ok
# find keypoint matches
idxs_cur, idxs_ref = match_frames(f_cur, f_ref, kFeatureMatchRatioTestInitializer)
print('|------------')
#print('deque ids: ', [f.id for f in self.frames])
print('initializing frames ', f_cur.id, ', ', f_ref.id)
print("# keypoint matches: ", len(idxs_cur))
Trc = self.estimatePose(f_ref.kpsn[idxs_ref], f_cur.kpsn[idxs_cur])
Tcr = inv_T(Trc) # Tcr w.r.t. ref frame
f_ref.update_pose(np.eye(4))
f_cur.update_pose(Tcr)
# remove outliers from keypoint matches by using the mask computed with inter frame pose estimation
mask_idxs = (self.mask_match.ravel() == 1)
self.num_inliers = sum(mask_idxs)
print('# keypoint inliers: ', self.num_inliers )
idx_cur_inliers = idxs_cur[mask_idxs]
idx_ref_inliers = idxs_ref[mask_idxs]
# create a temp map for initializing
map = Map()
f_ref.reset_points()
f_cur.reset_points()
#map.add_frame(f_ref)
#map.add_frame(f_cur)
kf_ref = KeyFrame(f_ref)
kf_cur = KeyFrame(f_cur, img_cur, segmentation=segmentation)
map.add_keyframe(kf_ref)
map.add_keyframe(kf_cur)
pts3d, mask_pts3d = triangulate_normalized_points(kf_cur.Tcw, kf_ref.Tcw, kf_cur.kpsn[idx_cur_inliers], kf_ref.kpsn[idx_ref_inliers])
new_pts_count, mask_points, _ = map.add_points(pts3d, mask_pts3d, kf_cur, kf_ref, idx_cur_inliers, idx_ref_inliers, img_cur, do_check=True, cos_max_parallax=Parameters.kCosMaxParallaxInitializer, segmentation=segmentation)
print("# triangulated points: ", new_pts_count)
if new_pts_count > self.num_min_triangulated_points:
err = map.optimize(verbose=False, rounds=20,use_robust_kernel=True)
print("init optimization error^2: %f" % err)
num_map_points = len(map.points)
print("# map points: %d" % num_map_points)
is_ok = num_map_points > self.num_min_triangulated_points
out.pts = pts3d[mask_points]
out.kf_cur = kf_cur
out.idxs_cur = idx_cur_inliers[mask_points]
out.kf_ref = kf_ref
out.idxs_ref = idx_ref_inliers[mask_points]
# set scene median depth to equal desired_median_depth'
desired_median_depth = Parameters.kInitializerDesiredMedianDepth
median_depth = kf_cur.compute_points_median_depth(out.pts)
depth_scale = desired_median_depth/median_depth
print('forcing current median depth ', median_depth,' to ',desired_median_depth)
out.pts[:,:3] = out.pts[:,:3] * depth_scale # scale points
tcw = kf_cur.tcw * depth_scale # scale initial baseline
kf_cur.update_translation(tcw)
map.delete()
if is_ok:
Printer.green('Inializer: ok!')
else:
self.num_failures += 1
Printer.red('Inializer: ko!')
print('|------------')
return out, is_ok