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initializer.py
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initializer.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 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):
ransac_method = None
try:
ransac_method = cv2.USAC_MSAC
except:
ransac_method = cv2.RANSAC
# 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=ransac_method, 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):
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)
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)
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