def handle_raw_stereo(self, msg):
size = (msg.left_info.width, msg.left_info.height)
if self.vo == None:
cam = camera.StereoCamera(msg.right_info)
self.fd = FeatureDetectorFast(300)
self.ds = DescriptorSchemeCalonder()
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.vo.num_frames = self.startframe
pair = [Image.fromstring("L", size, i.uint8_data.data) for i in [ msg.left_image, msg.right_image ]]
af = SparseStereoFrame(pair[0], pair[1], feature_detector = self.fd, descriptor_scheme = self.ds)
self.vo.handle_frame(af)
self.frame_timestamps[af.id] = msg.header.stamp
if self.skel.add(self.vo.keyframe):
print "====>", self.vo.keyframe.id, self.frame_timestamps[self.vo.keyframe.id]
#print self.tf.getFrameStrings()
#assert self.tf.frameExists("wide_stereo_optical_frame")
#assert self.tf.frameExists("odom_combined")
N = sorted(self.skel.nodes)
for a,b in zip(N, N[1:]):
if (a,b) in self.skel.edges:
t0 = self.frame_timestamps[a]
t1 = self.frame_timestamps[b]
if self.tf.canTransformFull("wide_stereo_optical_frame", t0, "wide_stereo_optical_frame", t1, "odom_combined"):
t,r = self.tf.lookupTransformFull("wide_stereo_optical_frame", t0, "wide_stereo_optical_frame", t1, "odom_combined")
relpose = Pose()
relpose.fromlist(self.tf.fromTranslationRotation(t,r))
self.wheel_odom_edges.add((a, b, relpose, 1.0))
self.send_map(msg.header.stamp)
def xtest_image_pan(self):
cam = camera.Camera((1.0, 1.0, 89.23, 320., 320., 240.0))
vo = VisualOdometer(cam)
prev_af = None
pose = None
im = Image.open("img1.pgm")
for x in [0,
5]: # range(0,100,10) + list(reversed(range(0, 100, 10))):
lf = im.crop((x, 0, x + 640, 480))
rf = im.crop((x, 0, x + 640, 480))
af = SparseStereoFrame(lf, rf)
if prev_af:
pairs = vo.temporal_match(prev_af, af)
pose = vo.solve(prev_af.kp, af.kp, pairs)
for i in range(10):
old = prev_af.kp[pairs[i][0]]
new = af.kp[pairs[i][1]]
print old, new, new[0] - old[0]
prev_af = af
print "frame", x, "has", len(af.kp), "keypoints", pose
if 0:
scribble = Image.merge("RGB",
(lf, rf, Image.new("L", lf.size))).resize(
(1280, 960))
#scribble = Image.merge("RGB", (Image.fromstring("L", lf.size, af0.lgrad),Image.fromstring("L", lf.size, af0.rgrad),Image.new("L", lf.size))).resize((1280,960))
draw = ImageDraw.Draw(scribble)
for (x, y, d) in af0.kp:
draw.line([(2 * x, 2 * y), (2 * x - 2 * d, 2 * y)],
fill=(255, 255, 255))
for (x, y, d) in af1.kp:
draw.line([(2 * x, 2 * y + 1), (2 * x - 2 * d, 2 * y + 1)],
fill=(0, 0, 255))
def xtest_smoke_bag(self):
import rosrecord
import visualize
class imgAdapted:
def __init__(self, i):
self.i = i
self.size = (i.width, i.height)
def tostring(self):
return self.i.data
cam = None
filename = "/u/prdata/videre-bags/loop1-mono.bag"
filename = "/u/prdata/videre-bags/vo1.bag"
framecounter = 0
for topic, msg in rosrecord.logplayer(filename):
print framecounter
if rospy.is_shutdown():
break
#print topic,msg
if topic == "/videre/cal_params" and not cam:
cam = camera.VidereCamera(msg.data)
vo = VisualOdometer(cam)
if cam and topic == "/videre/images":
if -1 <= framecounter and framecounter < 360:
imgR = imgAdapted(msg.images[0])
imgL = imgAdapted(msg.images[1])
af = SparseStereoFrame(imgL, imgR)
pose = vo.handle_frame(af)
visualize.viz(vo, af)
framecounter += 1
print "distance from start:", vo.pose.distance()
vo.summarize_timers()
def test_solve_spin(self):
# Test process with one 'ideal' camera, one real-world Videre
camera_param_list = [
(200.0, 200.0, 3.00, 320.0, 320.0, 240.0),
(389.0, 389.0, 89.23, 323.42, 323.42, 274.95),
]
for cam_params in camera_param_list:
cam = camera.Camera(cam_params)
vo = VisualOdometer(cam)
kps = []
model = [(x * 200, y * 200, z * 200) for x in range(-3, 4)
for y in range(-3, 4) for z in range(-3, 4)]
for angle in range(80):
im = Image.new("L", (640, 480))
theta = (angle / 80.) * (pi * 2)
R = rotation(theta, 0, 1, 0)
kp = []
for (mx, my, mz) in model:
pp = None
pt_camera = (numpy.dot(numpy.array([mx, my, mz]), R))
(cx, cy, cz) = numpy.array(pt_camera).ravel()
if cz > 100:
(x, y, d) = cam.cam2pix(cx, cy, cz)
if 0 <= x and x < 640 and 0 <= y and y < 480:
pp = (x, y, d)
circle(im, x, y, 2, 255)
kp.append(pp)
kps.append(kp)
expected_rot = numpy.array(
numpy.mat(rotation(2 * pi / 80, 0, 1, 0))).ravel()
for i in range(100):
i0 = i % 80
i1 = (i + 1) % 80
pairs = [(i, i) for i in range(len(model))
if (kps[i0][i] and kps[i1][i])]
def sanify(L, sub):
return [i or sub for i in L]
(inliers, rot, shift) = vo.solve(sanify(kps[i0], (0, 0, 0)),
sanify(kps[i1], (0, 0, 0)),
pairs)
self.assert_(inliers != 0)
self.assertAlmostEqual(shift[0], 0.0, 2)
self.assertAlmostEqual(shift[1], 0.0, 2)
self.assertAlmostEqual(shift[2], 0.0, 2)
for (et, at) in zip(rot, expected_rot):
self.assertAlmostEqual(et, at, 2)
def xtest_smoke(self):
cam = camera.Camera((389.0, 389.0, 89.23, 323.42, 323.42, 274.95))
vo = VisualOdometer(cam)
vo.reset_timers()
dir = "/u/konolige/vslam/data/indoor1/"
trail = []
prev_scale = None
schedule = [(f + 1000)
for f in (range(0, 100) + range(100, 0, -1) + [0] * 10)]
schedule = range(1507)
schedule = range(30)
for f in schedule:
lf = Image.open("%s/left-%04d.ppm" % (dir, f))
rf = Image.open("%s/right-%04d.ppm" % (dir, f))
lf.load()
rf.load()
af = SparseStereoFrame(lf, rf)
vo.handle_frame(af)
print f, vo.inl
trail.append(numpy.array(vo.pose.M[0:3, 3].T)[0])
def d(a, b):
d = a - b
return sqrt(numpy.dot(d, d.conj()))
print "covered ", sum([d(a, b) for (a, b) in zip(trail, trail[1:])])
print "from start", d(trail[0], trail[-1]), trail[0] - trail[-1]
vo.summarize_timers()
print vo.log_keyframes
def test_pe(self):
random.seed(0)
cloud = [(4 * (random.random() - 0.5), 4 * (random.random() - 0.5),
4 * (random.random() - 0.5)) for i in range(20)]
vo = VisualOdometer(
camera.Camera(
(389.0, 389.0, 89.23 * 1e-3, 323.42, 323.42, 274.95)))
stereo_cam = {}
af = {}
fd = FeatureDetectorStar(300)
ds = DescriptorSchemeSAD()
for i in range(5):
stereo_cam[i] = camera.Camera(
(389.0, 389.0, .080 + .010 * i, 323.42, 323.42, 274.95))
desired_pose = Pose()
cam = ray_camera(desired_pose, stereo_cam[i])
imL = Image.new("RGB", (640, 480))
imR = Image.new("RGB", (640, 480))
scene = []
scene += [
object(isphere(vec3(0, 0, 0), 1000), shadeLitCloud,
{'scale': 0.001})
]
scene += [
object(isphere(vec3(x, y, z + 6), 0.3), shadeLitCloud,
{'scale': 3}) for (x, y, z) in cloud
]
imL, imR = [im.convert("L") for im in [imL, imR]]
render_stereo_scene(imL, imR, None, cam, scene, 0)
af[i] = SparseStereoFrame(imL,
imR,
feature_detector=fd,
descriptor_scheme=ds)
vo.process_frame(af[i])
pe = PoseEstimator()
for i1 in range(5):
for i0 in range(5):
pairs = vo.temporal_match(af[i1], af[i0])
res = pe.estimateC(stereo_cam[i0], af[i0].features(),
stereo_cam[i1], af[i1].features(), pairs,
False)
x, y, z = res[2]
self.assertAlmostEqual(x, 0, 0)
self.assertAlmostEqual(y, 0, 0)
self.assertAlmostEqual(z, 0, 0)
def test_solve_rotation(self):
cam = camera.Camera((389.0, 389.0, 89.23, 323.42, 323.42, 274.95))
vo = VisualOdometer(cam)
model = []
radius = 1200.0
kps = []
for angle in range(80):
im = Image.new("L", (640, 480))
theta = (angle / 80.) * (pi * 2)
R = rotation(theta, 0, 1, 0)
kp = []
for s in range(7):
for t in range(7):
y = -400
pt_model = numpy.array([110 * (s - 3), y,
110 * (t - 3)]).transpose()
pt_camera = (numpy.dot(pt_model, R) +
numpy.array([0, 0, radius])).transpose()
(cx, cy, cz) = [float(i) for i in pt_camera]
(x, y, d) = cam.cam2pix(cx, cy, cz)
reversed = cam.pix2cam(x, y, d)
self.assertAlmostEqual(cx, reversed[0], 3)
self.assertAlmostEqual(cy, reversed[1], 3)
self.assertAlmostEqual(cz, reversed[2], 3)
kp.append((x, y, d))
circle(im, x, y, 2, 255)
kps.append(kp)
expected_shift = 2 * radius * sin(pi / 80)
for i in range(100):
i0 = i % 80
i1 = (i + 1) % 80
pairs = zip(range(len(kps[i0])), range(len(kps[i1])))
(inliers, rod, shift) = vo.solve(kps[i0], kps[i1], pairs)
actual_shift = sqrt(shift[0] * shift[0] + shift[1] * shift[1] +
shift[2] * shift[2])
# Should be able to estimate camera shift to nearest thousandth of mm
self.assertAlmostEqual(actual_shift, expected_shift, 2)
def __init__(self, source):
self.cvim = None
# These variables are internal:
self.stereo_cam = source.cam()
print "Camera is", self.stereo_cam
self.connected = False
self.snail_trail = []
self.source = source
self.inlier_history = []
self.label = 0
self.skel_dist_thresh = 0.5; # in meters
# These variables can be tweaked:
self.fd = FeatureDetectorFast(300)
# self.fd = FeatureDetectorStar(300)
self.ds = DescriptorSchemeCalonder(32,16) # set up lower sequential search window
self.camera_preview = False
self.vo = VisualOdometer(self.stereo_cam,
scavenge = False,
position_keypoint_thresh = 0.3,
angle_keypoint_thresh = 10*pi/180,
inlier_thresh = 100,
sba=None,
num_ransac_iters=500,
inlier_error_threshold = 3.0)
self.skel = Skeleton(self.stereo_cam,
link_thresh = 100,
descriptor_scheme = self.ds,
optimize_after_addition = False)
self.skel.node_vdist = 0
self.running = -1 # run forever
if self.camera_preview:
cv.NamedWindow("Camera")
cv.MoveWindow("Camera", 0, 700)
class Demo:
def __init__(self, source):
self.cvim = None
# These variables are internal:
self.stereo_cam = source.cam()
print "Camera is", self.stereo_cam
self.connected = False
self.snail_trail = []
self.source = source
self.inlier_history = []
self.label = 0
self.skel_dist_thresh = 0.1; # in meters
# These variables can be tweaked:
self.fd = FeatureDetectorFast(300)
# self.fd = FeatureDetectorStar(500)
self.ds = DescriptorSchemeCalonder()
self.camera_preview = False
self.vo = VisualOdometer(self.stereo_cam,
scavenge = False,
position_keypoint_thresh = 0.1,
angle_keypoint_thresh = 3*pi/180,
sba=None,
num_ransac_iters=500,
inlier_error_threshold = 3.0)
self.skel = Skeleton(self.stereo_cam,
link_thresh = 100,
descriptor_scheme = self.ds,
optimize_after_addition = False)
self.skel.node_vdist = 0
self.running = -1 # run forever
if self.camera_preview:
cv.NamedWindow("Camera")
cv.MoveWindow("Camera", 0, 700)
def handleFrame(self):
r = False
(w,h,li,ri) = self.source.getImage()
self.f = SparseStereoFrame(li, ri,
disparity_range = 96,
feature_detector = self.fd,
descriptor_scheme = self.ds)
self.vo.handle_frame(self.f)
print self.vo.inl
self.inlier_history = self.inlier_history[-20:] + [self.vo.inl]
# If the VO inlier count falls below 20, we're not well-connected for this link
if self.vo.inl < 20:
self.connected = False
self.f.pose = Pose()
self.snail_trail = []
# Add a frame to graph if:
# Frame zero, or
# There have been N successful preceding frames, and either
# Not connected,
# or if connected, the distance from the youngest graph frame to this frame is > thresh
add_to_graph = None
if self.vo.keyframe.id == 0 and len(self.skel.nodes) == 0:
add_to_graph = "Zero keyframe"
elif min(self.inlier_history) > 20:
if not self.connected:
add_to_graph = "Unconnected - but now recovering good poses"
# This is a new graph, so generate a new label
self.label += 1
else:
relpose = ~self.last_added_pose * self.f.pose
if relpose.distance() > self.skel_dist_thresh:
add_to_graph = "Connected and distance thresh passed"
if add_to_graph:
self.skel.setlabel(self.label)
self.skel.add(self.vo.keyframe, self.connected)
print "ADD %d %s" % (self.f.id, add_to_graph)
self.connected = True
self.last_added_pose = self.f.pose
self.snail_trail = []
# self.ioptimize(10)
self.snail_trail.append(self.f.pose)
# Render the camera view using OpenCV, then load it into an OpenGL texture
if True:
if not self.cvim:
self.cvim = cv.CreateImage((w,h/2), cv.IPL_DEPTH_8U, 3)
im = cv.CreateImage((w,h), cv.IPL_DEPTH_8U, 1)
im2 = cv.CreateImage((w/2,h/2), cv.IPL_DEPTH_8U, 1)
for i,src in enumerate([li, ri]):
cv.SetData(im, str(src.tostring()), w)
cv.Resize(im, im2)
cv.SetImageROI(self.cvim, (i * w / 2, 0, w / 2, h / 2))
cv.CvtColor(im2, self.cvim, cv.CV_GRAY2BGR)
cv.ResetImageROI(self.cvim)
def annotate(cvim):
green = cv.RGB(0,255,0)
red = cv.RGB(0,0,255)
def half(p):
return (p[0] / 2, p[1] / 2)
for (x,y,d) in self.f.features():
cv.Circle(cvim, half((x, y)), 2, green)
d = int(d)
cv.Line(cvim, ((w+x-d)/2, y/2 - 2), ((w+x-d)/2, y/2 + 2), green)
a_features = self.vo.keyframe.features()
b_features = self.f.features()
inliers = set([ (b,a) for (a,b) in self.vo.pe.inl])
outliers = set(self.vo.pairs) - inliers
for (a,b) in inliers:
cv.Line(cvim, half(b_features[b]), half(a_features[a]), green)
for (a,b) in outliers:
cv.Line(cvim, half(b_features[b]), half(a_features[a]), red)
annotate(self.cvim)
glBindTexture(GL_TEXTURE_2D, demo.textures[2])
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB8, 640, 240, 0, GL_RGB, GL_UNSIGNED_BYTE, self.cvim.tostring())
if self.camera_preview:
cv.ShowImage("Camera", self.cvim)
if cv.WaitKey(10) == 27:
r = True
return r
def anchor(self):
""" Return the current pose of the most recently added skeleton node """
id = max(self.skel.nodes)
return self.skel.newpose(id)
def optimize(self):
self.skel.optimize(1000)
def ioptimize(self,iters = 30):
self.skel.ioptimize(iters)
def report(self):
print
print
print
print "-" * 80
print self.skel.edges
print "-" * 80
def pose(self):
""" Return the current camera pose in the skeleton frame """
if len(self.skel.nodes) == 0:
return self.vo.pose
else:
return self.anchor() * (~self.snail_trail[0] * self.snail_trail[-1])
def map_size(self):
xyz = [demo.skel.newpose(i).xform(0,0,0) for i in demo.skel.nodes]
xrange = max([x for (x,y,z) in xyz]) - min([x for (x,y,z) in xyz])
zrange = max([z for (x,y,z) in xyz]) - min([z for (x,y,z) in xyz])
return max(xrange, zrange)
class RoadmapServer:
def __init__(self, args):
rospy.init_node('roadmap_server')
self.optimization_distance = rospy.get_param('~optimization_distance', 10);
self.tf = TransformListener()
stereo_cam = camera.Camera((389.0, 389.0, 89.23 * 1e-3, 323.42, 323.42, 274.95))
self.skel = Skeleton(stereo_cam)
# self.skel.node_vdist = 1
if False:
self.skel.load(args[1])
self.skel.optimize()
self.startframe = 100000
else:
self.startframe = 0
self.frame_timestamps = {}
self.vo = None
self.pub = rospy.Publisher("roadmap", vslam.msg.Roadmap)
time.sleep(1)
#self.send_map(rospy.time(0))
self.wheel_odom_edges = set()
rospy.Subscriber('/wide_stereo/raw_stereo', stereo_msgs.msg.RawStereo, self.handle_raw_stereo, queue_size=2, buff_size=7000000)
def send_map(self, stamp):
if len(self.skel.nodes) < 4:
return
TG = nx.MultiDiGraph()
for i in self.skel.nodes:
TG.add_node(i)
for (a,b,p,c) in self.skel.every_edge + list(self.wheel_odom_edges):
TG.add_edge(a, b, (p, c))
here = max(self.skel.nodes)
# TG is the total graph, G is the local subgraph
uTG = TG.to_undirected()
print "uTG", uTG.nodes(), uTG.edges()
close = set([here])
for i in range(self.optimization_distance):
close |= set(nx.node_boundary(uTG, close))
print "close", close
G = TG.subgraph(close)
uG = uTG.subgraph(close)
pg = TreeOptimizer3()
def mk_covar(xyz, rp, yaw):
return (1.0 / math.sqrt(xyz),1.0 / math.sqrt(xyz), 1.0 / math.sqrt(xyz), 1.0 / math.sqrt(rp), 1.0 / math.sqrt(rp), 1.0 / math.sqrt(yaw))
weak = mk_covar(9e10,3,3)
strong = mk_covar(0.0001, 0.000002, 0.00002)
pg.initializeOnlineOptimization()
def pgadd(p, n, data):
relpose, strength = data
if strength == 0.0:
cov = weak
else:
cov = strong
pg.addIncrementalEdge(p, n, relpose.xform(0,0,0), relpose.euler(), cov)
Gedges = G.edges(data = True)
revmap = {}
map = {}
for n in nx.dfs_preorder(uG, here):
revmap[len(map)] = n
map[n] = len(map)
priors = [p for p in uG.neighbors(n) if p in map]
if priors == []:
print "START NODE", n, "as", map[n]
else:
print "NEXT NODE", n
p = priors[0]
if not G.has_edge(p, n):
n,p = p,n
data = G.get_edge_data(p, n)[0]
Gedges.remove((p, n, data))
pgadd(map[p], map[n], data)
for (p,n,d) in Gedges:
pgadd(map[p], map[n], d)
pg.initializeOnlineIterations()
start_error = pg.error()
for i in range(10):
pg.iterate()
print "Error from", start_error, "to", pg.error()
pg.recomputeAllTransformations()
print self.tf.getFrameStrings()
target_frame = "base_link"
t = self.tf.getLatestCommonTime("wide_stereo_optical_frame", target_frame)
trans,rot = self.tf.lookupTransform(target_frame, "wide_stereo_optical_frame", t)
xp = Pose()
xp.fromlist(self.tf.fromTranslationRotation(trans,rot))
roadmap_nodes = dict([ (n, (30,0,0)) for n in TG.nodes() ])
nl = sorted(roadmap_nodes.keys())
print "nl", nl
for i in sorted(map.values()):
xyz,euler = pg.vertex(i)
p = from_xyz_euler(xyz, euler)
pose_in_target = xp * p
x,y,z = pose_in_target.xform(0,0,0)
euler = pose_in_target.euler()
print x,y,z, euler
roadmap_nodes[revmap[i]] = (x, y, euler[2])
roadmap_edges = []
for (p,n) in set(TG.edges()):
print p,n
best_length = max([ (confidence, pose.distance()) for (pose, confidence) in TG.get_edge_data(p, n).values()])[1]
roadmap_edges.append((p, n, best_length))
msg = vslam.msg.Roadmap()
msg.header.stamp = stamp
msg.header.frame_id = "base_link"
msg.nodes = [vslam.msg.Node(*roadmap_nodes[n]) for n in nl]
print "(p,n)", [ (p,n) for (p,n,l) in roadmap_edges ]
msg.edges = [vslam.msg.Edge(nl.index(p), nl.index(n), l) for (p, n, l) in roadmap_edges]
msg.localization = nl.index(here)
self.pub.publish(msg)
if 1:
import matplotlib.pyplot as plt
fig = plt.figure(figsize = (12, 6))
plt.subplot(121)
pos = nx.spring_layout(TG, iterations=1000)
nx.draw(TG, pos,node_color='#A0CBE2')
nx.draw_networkx_edges(G, pos, with_labels=False, edge_color='r', alpha=0.5, width=25.0, arrows = False)
plt.subplot(122)
nx.draw(G, pos = dict([(n, roadmap_nodes[n][:2]) for n in G.nodes()]))
#plt.show()
plt.savefig("/tmp/map_%d.png" % here)
def handle_raw_stereo(self, msg):
size = (msg.left_info.width, msg.left_info.height)
if self.vo == None:
cam = camera.StereoCamera(msg.right_info)
self.fd = FeatureDetectorFast(300)
self.ds = DescriptorSchemeCalonder()
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.vo.num_frames = self.startframe
pair = [Image.fromstring("L", size, i.uint8_data.data) for i in [ msg.left_image, msg.right_image ]]
af = SparseStereoFrame(pair[0], pair[1], feature_detector = self.fd, descriptor_scheme = self.ds)
self.vo.handle_frame(af)
self.frame_timestamps[af.id] = msg.header.stamp
if self.skel.add(self.vo.keyframe):
print "====>", self.vo.keyframe.id, self.frame_timestamps[self.vo.keyframe.id]
#print self.tf.getFrameStrings()
#assert self.tf.frameExists("wide_stereo_optical_frame")
#assert self.tf.frameExists("odom_combined")
N = sorted(self.skel.nodes)
for a,b in zip(N, N[1:]):
if (a,b) in self.skel.edges:
t0 = self.frame_timestamps[a]
t1 = self.frame_timestamps[b]
if self.tf.canTransformFull("wide_stereo_optical_frame", t0, "wide_stereo_optical_frame", t1, "odom_combined"):
t,r = self.tf.lookupTransformFull("wide_stereo_optical_frame", t0, "wide_stereo_optical_frame", t1, "odom_combined")
relpose = Pose()
relpose.fromlist(self.tf.fromTranslationRotation(t,r))
self.wheel_odom_edges.add((a, b, relpose, 1.0))
self.send_map(msg.header.stamp)
im = Image.merge("RGB", (imL, imR, imR))
im.save("/tmp/out%06d.png" % i)
imD[0].save("/tmp/out%06d-x.tiff" % i)
imD[1].save("/tmp/out%06d-y.tiff" % i)
imD[2].save("/tmp/out%06d-z.tiff" % i)
else:
im = Image.open("/tmp/out%06d.png" % i)
imL, imR, _ = im.split()
return (desired_pose, imL, imR)
fd = FeatureDetectorFast(300)
ds = DescriptorSchemeCalonder()
skel = Skeleton(stereo_cam, descriptor_scheme=ds)
vo = VisualOdometer(stereo_cam,
scavenge=False,
sba=None,
inlier_error_threshold=3.0)
vo.num_frames = 0
ground_truth = []
started = time.time()
connected = False
for i in range(0, 800):
print i
(desired_pose, imL, imR) = getImage(i)
ground_truth.append(desired_pose.xform(0, 0, 0))
f = SparseStereoFrame(imL, imR, feature_detector=fd, descriptor_scheme=ds)
vo.handle_frame(f)
skel.add(vo.keyframe, connected)
connected = True
c_r = vop.where(ok, f1i, 0)
c_g = vop.where(ok, f1i0, 0)
return c_r, c_g
cv.NamedWindow("disparity", 1)
cv.NamedWindow("merged", 1)
cv.MoveWindow("merged", 0, 0)
cv.NamedWindow("diff", 1)
cv.MoveWindow("diff", 700, 0)
chain = []
for cam, L, R in r:
f1 = DenseStereoFrame(L, R, feature_detector=fd, descriptor_scheme=ds)
if chain == []:
vo = VisualOdometer(cam, ransac_iters=500)
vo.handle_frame(f1)
d_str = "".join([chr(min(x / 4, 255)) for x in f1.disp_values])
disparity = cv.CreateImage((640, 480), 8, 1)
cv.SetData(disparity, d_str, 640)
cv.ShowImage("disparity", disparity)
if len(chain) > 5:
f0 = chain[0]
img = cv.CreateImage((640, 480), 8, 3)
diffpose = ~f0.pose * f1.pose
a, b = img_img(f0, f1, diffpose.tolist(), cam)
ai = vop2iplimage(a)
bi = vop2iplimage(b)
# a is red
# b is green/blue
cv.Merge(bi, bi, ai, None, img)
Tx = 0.090 # baseline in M
Clx = 320 # Center left image
Crx = 320 # Center right image
Cy = 240 # Center Y (both images)
# Camera
cam = camera.Camera((Fx, Fy, Tx, Clx, Crx, Cy))
# Feature Detector
fd = FeatureDetectorFast(300)
# Descriptor Scheme
ds = DescriptorSchemeCalonder()
# Visual Odometer
vo = VisualOdometer(cam)
for i in range(1000):
# Left image
lim = Image.open("%s/%06dL.png" % (sys.argv[1], i))
# Right image
rim = Image.open("%s/%06dR.png" % (sys.argv[1], i))
# Combine the images to make a stereo frame
frame = SparseStereoFrame(lim,
rim,
feature_detector=fd,
descriptor_scheme=ds)
# Supply the stereo frame to the visual odometer
def xtest_sim(self):
# Test process with one 'ideal' camera, one real-world Videre
camera_param_list = [
# (200.0, 200.0, 3.00, 320.0, 320.0, 240.0),
(389.0, 389.0, 1e-3 * 89.23, 323.42, 323.42, 274.95)
]
def move_forward(i, prev):
""" Forward 1 meter, turn around, Back 1 meter """
if i == 0:
return Pose(rotation(0, 0, 1, 0), (0, 0, 0))
elif i < 10:
return prev * Pose(rotation(0, 0, 1, 0), (0, 0, .1))
elif i < 40:
return prev * Pose(rotation(math.pi / 30, 0, 1, 0), (0, 0, 0))
elif i < 50:
return prev * Pose(rotation(0, 0, 1, 0), (0, 0, .1))
for movement in [move_forward]: # move_combo, move_Yrot ]:
for cam_params in camera_param_list:
cam = camera.Camera(cam_params)
random.seed(0)
def rr():
return 2 * random.random() - 1.0
model = [(3 * rr(), 1 * rr(), 3 * rr()) for i in range(300)]
def rndimg():
b = "".join(random.sample([chr(c) for c in range(256)],
64))
return Image.fromstring("L", (8, 8), b)
def sprite(dst, x, y, src):
try:
dst.paste(src, (int(x) - 4, int(y) - 4))
except:
print "paste failed", x, y
palette = [rndimg() for i in model]
expected = []
afs = []
P = None
for i in range(50):
print "render", i
P = movement(i, P)
li = Image.new("L", (640, 480))
ri = Image.new("L", (640, 480))
q = 0
for (mx, my, mz) in model:
pp = None
pt_camera = (numpy.dot(P.M.I,
numpy.array([mx, my, mz, 1]).T))
(cx, cy, cz, cw) = numpy.array(pt_camera).ravel()
if cz > .100:
((xl, yl), (xr, yr)) = cam.cam2pixLR(cx, cy, cz)
if 0 <= xl and xl < 640 and 0 <= yl and yl < 480:
sprite(li, xl, yl, palette[q])
sprite(ri, xr, yr, palette[q])
q += 1
expected.append(P)
afs.append(SparseStereoFrame(imgStereo(li), imgStereo(ri)))
vo = VisualOdometer(cam)
for i, (af, ep) in enumerate(zip(afs, expected)):
vo.handle_frame(af)
if 0:
print vo.pose.xform(0, 0, 0)
print "expected", ep.M
print "vo.pose", vo.pose.M
print numpy.abs((ep.M - vo.pose.M))
self.assert_(
numpy.alltrue(numpy.abs((ep.M - vo.pose.M)) < 0.2))
return
def run(vos):
for af in afs:
for vo in vos:
vo.handle_frame(af)
# Check that the pose estimators are truly independent
v1 = VisualOdometer(cam,
feature_detector=FeatureDetectorFast(),
descriptor_scheme=DescriptorSchemeSAD(),
inlier_error_threshold=1.0)
v2 = VisualOdometer(cam,
feature_detector=FeatureDetectorFast(),
descriptor_scheme=DescriptorSchemeSAD(),
inlier_error_threshold=2.0)
v8 = VisualOdometer(cam,
feature_detector=FeatureDetectorFast(),
descriptor_scheme=DescriptorSchemeSAD(),
inlier_error_threshold=8.0)
v1a = VisualOdometer(cam,
feature_detector=FeatureDetectorFast(),
descriptor_scheme=DescriptorSchemeSAD(),
inlier_error_threshold=1.0)
run([v1])
run([v2, v8, v1a])
self.assert_(v1.pose.xform(0, 0, 0) == v1a.pose.xform(0, 0, 0))
for a, b in [(v1, v2), (v2, v8), (v1, v8)]:
self.assert_(a.pose.xform(0, 0, 0) != b.pose.xform(0, 0, 0))
return
log_dead_reckon = []
cold = True
inl_history = [0, 0]
for cam, l_image, r_image, label in playlist(args):
if vos:
print framecounter, len(skel.nodes), "skel nodes"
if vos == None:
#fd = FeatureDetectorFast(300)
fd = FeatureDetectorStar(300)
ds = DescriptorSchemeCalonder()
vos = [
VisualOdometer(cam,
scavenge=False,
inlier_error_threshold=3.0,
sba=None,
inlier_thresh=100,
ransac_iters=500,
position_keypoint_thresh=0.2,
angle_keypoint_thresh=0.15)
]
vo_x = [[] for i in vos]
vo_y = [[] for i in vos]
vo_u = [[] for i in vos]
vo_v = [[] for i in vos]
trajectory = [[] for i in vos]
skel = Skeleton(cam)
if skel_load_filename:
skel.load(skel_load_filename)
vos[0].num_frames = max(skel.nodes) + 1
framecounter = max(skel.nodes) + 1
# skel.fill("/u/prdata/vslam_data/FtCarson/2007.08/2007.08.29/course3-DTED4-run1/traj.txt", 118016)