class PicmapNode: def __init__(self, args): rospy.init_node('picmap_server') stereo_cam = camera.Camera( (389.0, 389.0, 89.23 * 1e-3, 323.42, 323.42, 274.95)) self.vo = None self.transformer = pytf_swig.pyTransformer() self.transformer.setExtrapolationLimit(0.0) self.fd = FeatureDetectorFast(300) self.ds = DescriptorSchemeCalonder() self.pm = PictureMap(self.ds) #self.pub = rospy.Publisher("/picmap_pose", vslam.msg.Picmap) rospy.Subscriber('/stereo/raw_stereo', stereo_msgs.msg.RawStereo, self.handle_raw_stereo_queue, queue_size=2, buff_size=7000000) rospy.Subscriber('/amcl_pose', geometry_msgs.msg.PoseWithCovarianceStamped, self.handle_amcl_pose) rospy.Subscriber('/tf_message', tf.msg.tfMessage, self.handle_tf_queue) self.pmlock = threading.Lock() self.q = Queue.Queue(0) # 0 means maxsize is infinite self.tfq = Queue.Queue(0) t = threading.Thread(target=self.worker) t.start() def start(self): rospy.spin() pickle.dump(self.pm, open("/tmp/final_pm.pickle", "w")) def handle_tf_queue(self, msg): print " queued transform", min( [t.header.stamp.to_seconds() for t in msg.transforms]) self.tfq.put(msg) def handle_tf(self, msg): #print "incoming transform", min([ t.header.stamp.to_seconds() for t in msg.transforms ]) for transform in msg.transforms: ptf = py_transform_from_transform_stamped(transform) self.transformer.setTransform(ptf) #print self.transformer.allFramesAsString() #print "TF", "map->stereo_link getLatestCommonTime", self.transformer.getLatestCommonTime('map', 'stereo_link') def handle_raw_stereo_queue(self, msg): print " queued picture ", msg.header.stamp.to_seconds() self.q.put(msg) def worker(self): while True: # There are N messages in the queue. # Want to discard all but the most recent. while self.q.qsize() > 1: self.q.get() item = self.q.get() self.handle_raw_stereo(item) def handle_raw_stereo(self, msg): print "incoming picture ", msg.header.stamp.to_seconds() size = (msg.left_info.width, msg.left_info.height) cam = camera.StereoCamera(msg.right_info) if self.vo == None: self.vo = VisualOdometer(cam, scavenge=False, inlier_error_threshold=3.0, sba=None, inlier_thresh=100, position_keypoint_thresh=0.2, angle_keypoint_thresh=0.15) self.keys = set() self.v = Vis('raw stereo') #pair = [Image.fromstring("L", size, i.uint8_data.data) for i in [ msg.left_image, msg.right_image ]] pair = [dcamImage(i) for i in [msg.left_image, msg.right_image]] af = SparseStereoFrame(pair[0], pair[1], feature_detector=self.fd, descriptor_scheme=self.ds) af.t = msg.header.stamp.to_seconds() self.vo.handle_frame(af) self.v.show(msg.left_image.uint8_data.data, []) k = self.vo.keyframe if (not (k.id in self.keys)): self.keys.add(k.id) picture = Picture(k.features(), k.descriptors()) self.pm.newpic(k.t, cam, picture, True) if 0: picture = Picture(af.features(), af.descriptors()) self.pm.newpic(af.t, cam, picture, False) while True: if self.tfq.qsize() == 0: break tfmsg = self.tfq.get() if min([t.header.stamp.to_seconds() for t in tfmsg.transforms ]) > (msg.header.stamp.to_seconds() + 1.0): break self.handle_tf(tfmsg) self.pmlock.acquire() self.pm.resolve(self.transformer) self.pmlock.release() def handle_amcl_pose(self, msg): if self.vo: print "handle_amcl_pose(", msg, ")" happy = max([msg.pose.covariance[0], msg.pose.covariance[7] ]) < 0.003 print "picmap node got amcl", msg.header.stamp.to_seconds( ), msg.pose.covariance[0], msg.pose.covariance[7], "happy", happy self.pmlock.acquire() self.pm.newLocalization(msg.header.stamp.to_seconds(), happy) self.pmlock.release()
class VODemo: vo = None frame = 0 def __init__(self, mode, library): self.mode = mode self.library = library rospy.TopicSub('/videre/images', ImageArray, self.display_array) rospy.TopicSub('/videre/cal_params', String, self.display_params) rospy.TopicSub('/vo/tmo', Pose44, self.handle_corrections) self.viz_pub = rospy.Publisher("/overlay", Lines) self.vo_key_pub = rospy.Publisher("/vo/key", Frame) self.Tmo = Pose() self.mymarker1 = Marker(10) self.mymarkertrail = [ Marker(11 + i) for i in range(10) ] self.trail = [] self.vis = Vis() def display_params(self, iar): if not self.vo: cam = camera.VidereCamera(iar.data) print "cam.params", cam.params self.vo = VisualOdometer(cam) self.started = None if self.mode == 'learn': self.library = set() self.previous_keyframe = None self.know_state = 'lost' self.best_show_pose = None self.mymarker1.floor() def handle_corrections(self, corrmsg): print "GOT CORRECTION AT", time.time() Tmo_pose = Pose() Tmo_pose.fromlist(corrmsg.v) self.Tmo = Tmo_pose self.know_state = 'corrected' def display_array(self, iar): diag = "" af = None if self.vo: if not self.started: self.started = time.time() imgR = imgAdapted(iar.images[0]) imgL = imgAdapted(iar.images[1]) af = SparseStereoFrame(imgL, imgR) if 1: if self.mode == 'play': pose = self.vo.handle_frame(af) if self.mode == 'learn': pose = self.vo.handle_frame(af) if (af.id != 0) and (self.vo.inl < 80): print "*** LOST TRACK ***" #sys.exit(1) self.library.add(self.vo.keyframe) else: #diag = "best match %d from %d in library" % (max(probes)[0], len(self.library)) pass diag = "%d/%d inliers, moved %.1f library size %d" % (self.vo.inl, len(af.kp), pose.distance(), len(self.library)) if self.mode == 'play': kf = self.vo.keyframe if kf != self.previous_keyframe: f = Frame() f.id = kf.id f.pose = Pose44(kf.pose.tolist()) f.keypoints = [ Keypoint(x,y,d) for (x,y,d) in kf.kp ] f.descriptors = [ Descriptor(d) for d in kf.descriptors ] print "ASKING FOR MATCH AT", time.time() self.vo_key_pub.publish(f) self.previous_keyframe = kf if kf.inl < 50 or self.vo.inl < 50: self.know_state = 'lost' else: self.know_state = { 'lost':'lost', 'uncertain':'uncertain', 'corrected':'uncertain' }[self.know_state] result_pose = af.pose if self.mode == 'learn': self.mymarker1.frompose(af.pose, self.vo.cam, (255,255,255)) else: if self.best_show_pose and self.know_state == 'lost': inmap = self.best_show_pose else: Top = af.pose Tmo = self.Tmo inmap = Tmo * Top if self.know_state != 'lost': self.best_show_pose = inmap if self.know_state != 'lost' or not self.best_show_pose: color = { 'lost' : (255,0,0), 'uncertain' : (127,127,0), 'corrected' : (0,255,0) }[self.know_state] self.mymarker1.frompose(inmap, self.vo.cam, color) if 0: self.trail.append(inmap) self.trail = self.trail[-10:] for i,p in enumerate(self.trail): self.mymarkertrail[i].frompose(p, color) #print af.diff_pose.xform(0,0,0), af.pose.xform(0,0,0) if self.frame > 5 and ((self.frame % 10) == 0): inliers = self.vo.pe.inliers() pts = [(1,int(x0),int(y0)) for ((x0,y0,d0), (x1,y1,d1)) in inliers] self.vis.show(iar.images[1].data, pts ) if False and self.vo.pairs != []: ls = Lines() inliers = self.vo.pe.inliers() lr = "left_rectified" ls.lines = [ Line(lr, 0,255,0,x0,y0-2,x0,y0+2) for ((x0,y0,d0), (x1,y1,d1)) in inliers] ls.lines += [ Line(lr, 0,255,0,x0-2,y0,x0+2,y0) for ((x0,y0,d0), (x1,y1,d1)) in inliers] rr = "right_rectified" #ls.lines += [ Line(rr, 0,255,0,x0-d0,y0-2,x0-d0,y0+2) for ((x0,y0,d0), (x1,y1,d1)) in inliers] #ls.lines += [ Line(rr, 0,255,0,x0-2-d0,y0,x0+2-d0,y0) for ((x0,y0,d0), (x1,y1,d1)) in inliers] self.viz_pub.publish(ls) if (self.frame % 30) == 0: took = time.time() - self.started print "%4d: %5.1f [%f fps]" % (self.frame, took, self.frame / took), diag self.frame += 1 #print "got message", len(iar.images) #print iar.images[0].width if SEE: right = ut.ros2cv(iar.images[0]) left = ut.ros2cv(iar.images[1]) hg.cvShowImage('channel L', left) hg.cvShowImage('channel R', right) hg.cvWaitKey(5) def dump(self): print print self.vo.name() self.vo.summarize_timers() if self.mode == 'learn': print "DUMPING LIBRARY", len(self.library) f = open("library.pickle", "w") pickle.dump(self.vo.cam, f) db = [(af.id, af.pose, af.kp, af.descriptors) for af in self.library] pickle.dump(db, f) f.close() print "DONE"
class VODemo: vo = None frame = 0 def __init__(self, mode, library): self.mode = mode self.library = library rospy.TopicSub('/videre/images', ImageArray, self.display_array) rospy.TopicSub('/videre/cal_params', String, self.display_params) rospy.TopicSub('/vo/tmo', Pose44, self.handle_corrections) self.viz_pub = rospy.Publisher("/overlay", Lines) self.vo_key_pub = rospy.Publisher("/vo/key", Frame) self.Tmo = Pose() self.mymarker1 = Marker(10) self.mymarkertrail = [Marker(11 + i) for i in range(10)] self.trail = [] self.vis = Vis() def display_params(self, iar): if not self.vo: cam = camera.VidereCamera(iar.data) print "cam.params", cam.params self.vo = VisualOdometer(cam) self.started = None if self.mode == 'learn': self.library = set() self.previous_keyframe = None self.know_state = 'lost' self.best_show_pose = None self.mymarker1.floor() def handle_corrections(self, corrmsg): print "GOT CORRECTION AT", time.time() Tmo_pose = Pose() Tmo_pose.fromlist(corrmsg.v) self.Tmo = Tmo_pose self.know_state = 'corrected' def display_array(self, iar): diag = "" af = None if self.vo: if not self.started: self.started = time.time() imgR = imgAdapted(iar.images[0]) imgL = imgAdapted(iar.images[1]) af = SparseStereoFrame(imgL, imgR) if 1: if self.mode == 'play': pose = self.vo.handle_frame(af) if self.mode == 'learn': pose = self.vo.handle_frame(af) if (af.id != 0) and (self.vo.inl < 80): print "*** LOST TRACK ***" #sys.exit(1) self.library.add(self.vo.keyframe) else: #diag = "best match %d from %d in library" % (max(probes)[0], len(self.library)) pass diag = "%d/%d inliers, moved %.1f library size %d" % ( self.vo.inl, len(af.kp), pose.distance(), len( self.library)) if self.mode == 'play': kf = self.vo.keyframe if kf != self.previous_keyframe: f = Frame() f.id = kf.id f.pose = Pose44(kf.pose.tolist()) f.keypoints = [ Keypoint(x, y, d) for (x, y, d) in kf.kp ] f.descriptors = [Descriptor(d) for d in kf.descriptors] print "ASKING FOR MATCH AT", time.time() self.vo_key_pub.publish(f) self.previous_keyframe = kf if kf.inl < 50 or self.vo.inl < 50: self.know_state = 'lost' else: self.know_state = { 'lost': 'lost', 'uncertain': 'uncertain', 'corrected': 'uncertain' }[self.know_state] result_pose = af.pose if self.mode == 'learn': self.mymarker1.frompose(af.pose, self.vo.cam, (255, 255, 255)) else: if self.best_show_pose and self.know_state == 'lost': inmap = self.best_show_pose else: Top = af.pose Tmo = self.Tmo inmap = Tmo * Top if self.know_state != 'lost': self.best_show_pose = inmap if self.know_state != 'lost' or not self.best_show_pose: color = { 'lost': (255, 0, 0), 'uncertain': (127, 127, 0), 'corrected': (0, 255, 0) }[self.know_state] self.mymarker1.frompose(inmap, self.vo.cam, color) if 0: self.trail.append(inmap) self.trail = self.trail[-10:] for i, p in enumerate(self.trail): self.mymarkertrail[i].frompose(p, color) #print af.diff_pose.xform(0,0,0), af.pose.xform(0,0,0) if self.frame > 5 and ((self.frame % 10) == 0): inliers = self.vo.pe.inliers() pts = [(1, int(x0), int(y0)) for ((x0, y0, d0), (x1, y1, d1)) in inliers] self.vis.show(iar.images[1].data, pts) if False and self.vo.pairs != []: ls = Lines() inliers = self.vo.pe.inliers() lr = "left_rectified" ls.lines = [ Line(lr, 0, 255, 0, x0, y0 - 2, x0, y0 + 2) for ((x0, y0, d0), (x1, y1, d1)) in inliers ] ls.lines += [ Line(lr, 0, 255, 0, x0 - 2, y0, x0 + 2, y0) for ((x0, y0, d0), (x1, y1, d1)) in inliers ] rr = "right_rectified" #ls.lines += [ Line(rr, 0,255,0,x0-d0,y0-2,x0-d0,y0+2) for ((x0,y0,d0), (x1,y1,d1)) in inliers] #ls.lines += [ Line(rr, 0,255,0,x0-2-d0,y0,x0+2-d0,y0) for ((x0,y0,d0), (x1,y1,d1)) in inliers] self.viz_pub.publish(ls) if (self.frame % 30) == 0: took = time.time() - self.started print "%4d: %5.1f [%f fps]" % (self.frame, took, self.frame / took), diag self.frame += 1 #print "got message", len(iar.images) #print iar.images[0].width if SEE: right = ut.ros2cv(iar.images[0]) left = ut.ros2cv(iar.images[1]) hg.cvShowImage('channel L', left) hg.cvShowImage('channel R', right) hg.cvWaitKey(5) def dump(self): print print self.vo.name() self.vo.summarize_timers() if self.mode == 'learn': print "DUMPING LIBRARY", len(self.library) f = open("library.pickle", "w") pickle.dump(self.vo.cam, f) db = [(af.id, af.pose, af.kp, af.descriptors) for af in self.library] pickle.dump(db, f) f.close() print "DONE"
for fr in frames1: vo.check_inliers(af, fr) print "Inliers: ", vo.inl # vo.handle_frame(af) x, y, z = vo.pose.xform(0, 0, 0) trajectory.append((x, y, z)) vo_x.append(x) vo_y.append(z) x1, y1, z1 = vo.pose.xform(0, 0, 1) vo_u.append(x1 - x) vo_v.append(z1 - z) inliers = vo.pe.inliers() pts = [(1, int(x0), int(y0)) for ((x0, y0, d0), (x1, y1, d1)) in inliers] vis.show(msg.left_image.byte_data.data, pts) print "pose", framecounter, vo.inl, x, y, z # optional show the plot c = checkch() if not c == None: if ploton: ploton = 0 pylab.ioff() else: ploton = 1 pylab.ion() if ploton and len(vo.log_keyframes) > initfig: ploton -= 1