def main():
parser = argparse.ArgumentParser(description='Estimate orientation offset between body and cam on gimbal')
parser.add_argument('--res', required=True, help='path to result pickle')
parser.add_argument('--skip', default=0, type=int, help='skip first x frames')
parser.add_argument('--huber-coef', default=0, type=float, help='use pseudo huber loss')
parser.add_argument('--nadir-looking', action='store_true', help='better plot for nadir looking cam')
parser.add_argument('--init-ypr', default=[0.0, 0.0, 0.0], type=float, nargs=3)
args = parser.parse_args()
with open(args.res, 'rb') as fh:
results, map3d, frame_names, meta_names, ground_truth, *_ = pickle.load(fh)
# (kf.pose, kf.measure, kf.time, kf.id)
meas_q = np.array([tools.ypr_to_q(*np.flip(meas.data[3:6])) for pose, meas, _, _ in results if meas and pose.post])
est_q = np.array([(-pose.post).to_global(NokiaSensor.b2c).quat for pose, meas, _, _ in results if meas and pose.post])
meas_q = meas_q[args.skip:]
est_q = est_q[args.skip:]
print('optimizing orientation offset based on %d measurements...' % (len(meas_q),))
def costfun(x, meas_q, est_q):
off_q = np.quaternion(*x).normalized()
delta_angles = tools.angle_between_q_arr(est_q, meas_q * off_q)
return delta_angles
x0 = tools.ypr_to_q(*(np.array(args.init_ypr)/180*np.pi)).components
res = least_squares(costfun, x0, verbose=2, ftol=1e-5, xtol=1e-5, gtol=1e-8, max_nfev=1000,
loss='huber' if args.huber_coef > 0 else 'linear', f_scale=args.huber_coef or 1.0, #huber_coef,
args=(meas_q, est_q))
off_q = np.quaternion(*res.x).normalized() if 1 else quaternion.one
print('offset q: %s, ypr: %s' % (off_q, np.array(tools.q_to_ypr(off_q)) / np.pi * 180,))
args.nadir_looking = False # TODO: remove override
nl_dq = tools.eul_to_q((-np.pi / 2,), 'y') if args.nadir_looking else quaternion.one
est_ypr = np.array([tools.q_to_ypr(nl_dq.conj() * q) for q in est_q]) / np.pi * 180
corr_ypr = np.array([tools.q_to_ypr(nl_dq.conj() * q * off_q) for q in meas_q]) / np.pi * 180
plt.plot(est_ypr[:, 0], 'C0:')
plt.plot(est_ypr[:, 1], 'C1:')
plt.plot(est_ypr[:, 2], 'C2:')
plt.plot(corr_ypr[:, 0], 'C0-')
plt.plot(corr_ypr[:, 1], 'C1-')
plt.plot(corr_ypr[:, 2], 'C2-')
plt.show()
def initialize_frame(self, time, image, measure):
nf = super(VisualGPSNav, self).initialize_frame(time, image, measure)
if nf.measure:
old_kf = [kf for kf in self.state.keyframes if kf.measure]
nf.measure.time_adj = old_kf[-1].measure.time_adj if len(old_kf) > 0 else 0
geodetic_origin = self.geodetic_origin
lat, lon, alt = nf.measure.data[0:3]
roll, pitch, yaw = nf.measure.data[3:6]
b2c_roll, b2c_pitch, b2c_yaw = nf.measure.data[6:9]
# world frame: +z up, +x is east, +y is north
# body frame: +z down, +x is fw towards north, +y is right wing (east)
# camera frame: +z into the image plane, -y is up, +x is right
# wf2bf_q = tools.eul_to_q((np.pi, -np.pi / 2), 'xz')
# bf2cf = Pose(None, tools.eul_to_q((np.pi / 2, np.pi / 2), 'yz'))
if 1:
w2b_bf_q = tools.ypr_to_q(yaw, pitch, roll)
b2c_bf_q = tools.ypr_to_q(b2c_yaw, b2c_pitch, b2c_roll)
yaw, pitch, roll = tools.q_to_ypr(w2b_bf_q * b2c_bf_q * (self.ori_off_q or quaternion.one))
cf_w2c = tools.lla_ypr_to_loc_quat(geodetic_origin, [lat, lon, alt], [yaw, pitch, roll], b2c=self.bf2cf)
if len(old_kf) > 0:
cf_w2c.vel = (cf_w2c.loc - (-old_kf[-1].pose.prior).loc) / (nf.time - old_kf[-1].time).total_seconds()
else:
cf_w2c.vel = np.array([0, 0, 0])
nf.pose.prior = -cf_w2c
else:
wf_body_r = tools.to_cartesian(lat, lon, alt, *geodetic_origin)
bf_world_body_q = tools.ypr_to_q(yaw, pitch, roll)
if 0:
cf_world_q = bf_cam_q.conj() * bf_world_body_q.conj()
cf_body_r = tools.q_times_v(bf_cam_q.conj(), -bf_cam_r)
cf_body_world_r = tools.q_times_v(cf_world_q * self.wf2bf_q.conj(), -wf_body_r)
nf.pose.prior = Pose(cf_body_r + cf_body_world_r, cf_world_q * bf_cam_q)
else:
bf_world_q = bf_world_body_q.conj() * self.wf2bf_q.conj()
cf_world_q = bf_cam_q.conj() * bf_world_q
cf_body_r = tools.q_times_v(bf_cam_q.conj(), -bf_cam_r)
cf_body_world_r = tools.q_times_v(cf_world_q, -wf_body_r)
# bf_cam_q.conj() * bf_world_body_q.conj() * self.wf2bf_q.conj()
nf.pose.prior = Pose(cf_body_r + cf_body_world_r, bf_cam_q.conj() * bf_world_body_q.conj() * bf_cam_q)
# bf_cam_q.conj() * bf_world_body_q.conj() * bf_cam_q
# (NokiaSensor.w2b_q * NokiaSensor.b2c_q) * prior.quat.conj()
# self.wf2bf_q * bf_cam_q * (bf_cam_q.conj() * bf_world_body_q.conj() * bf_cam_q).conj()
# => self.wf2bf_q * bf_cam_q //*// bf_cam_q.conj() * bf_world_body_q * bf_cam_q //*//
# self.wf2bf_q * bf_cam_q //*// bf_cam_q.conj() * bf_world_body_q * bf_cam_q //*// bf_cam_q.conj() * bf_world_body_q.conj() * self.wf2bf_q.conj()
return nf
def render_itokawa(show=False):
cam = Camera(4096,
4096,
aperture=1.5,
focal_length=120.8,
sensor_size=[12.288] * 2,
px_saturation_e=30e3)
shape_model_file = [
r'C:\projects\sispo\data\models\itokawa_16k.obj',
r'C:\projects\sispo\data\models\itokawa_f3145728.obj',
][0]
RenderEngine.REFLMOD_PARAMS[RenderEngine.REFLMOD_HAPKE] = [
553.38, # J 0
26, # th_p 26
0.31, # w 0.31
-0.35, # b -0.35
0, # c 0
0.86, # B_SH0 0.86
0.00021, # hs 0.00021
0, # B_CB0 0
0.005, # hc 0.005
1, # K 1
]
re = RenderEngine(cam.width, cam.height,
antialias_samples=0) #, enable_extra_data=True)
re.set_frustum(cam.x_fov, cam.y_fov, 0.05, 12)
obj_idx = re.load_object(shape_model_file)
if 1:
g_sc_q = tools.angleaxis_to_q(
[1.847799, -0.929873, 0.266931, -0.253146])
#x, y, z = -13.182141, -64.694813, 116.263134
x, y, z = 93.818, -8.695, 1.263
g_ast_q = get_ast_q(x, y, z)
g_sol_ast_v = np.array([146226194732.0, -68812326932.0, -477863381.0
]) * 1e-3
g_sol_sc_v = np.array([146226188132.0, -68812322442.0, -477863711.0
]) * 1e-3
g_sc_ast_v = g_sol_ast_v - g_sol_sc_v
l_ast_sc_v = tools.q_times_v(
SystemModel.sc2gl_q.conj() * g_sc_q.conj(), g_sc_ast_v)
l_ast_q = SystemModel.sc2gl_q.conj() * g_sc_q.conj(
) * g_ast_q * SystemModel.sc2gl_q
l_light_v = tools.q_times_v(SystemModel.sc2gl_q.conj() * g_sc_q.conj(),
g_sol_ast_v / np.linalg.norm(g_sol_ast_v))
else:
l_ast_sc_v = np.array([0, 0, -7.990 * 1])
l_ast_q = tools.angleaxis_to_q((math.radians(20), 0, 1, 0))
l_light_v = np.array([1, 0, -1]) / math.sqrt(2)
sc_mode = 0
a, b, c = [0] * 3
ds, dq = 0.135, tools.ypr_to_q(math.radians(1.154), 0,
math.radians(-5.643))
# ds, dq = 0.535, quaternion.one # itokawa centered
while True:
img = re.render(obj_idx,
tools.q_times_v(dq, l_ast_sc_v * ds),
l_ast_q,
l_light_v,
flux_density=1.0,
gamma=1.0,
get_depth=False,
shadows=True,
textures=True,
reflection=RenderEngine.REFLMOD_HAPKE)
# data = re.render_extra_data(obj_idx, tools.q_times_v(dq, l_ast_sc_v*ds), l_ast_q, l_light_v)
# import matplotlib.pyplot as plt
# plt.imshow(data[:, :, 0])
k = output(img, show, maxval=0.90)
if k is None or k == 27:
break
tmp = 1 if k in (ord('a'), ord('s'), ord('q')) else -1
if k in (ord('a'), ord('d')):
if sc_mode:
dq = tools.ypr_to_q(math.radians(tmp * 0.033), 0, 0) * dq
else:
b += tmp
if k in (ord('w'), ord('s')):
if sc_mode:
dq = tools.ypr_to_q(0, 0, math.radians(tmp * 0.033)) * dq
else:
a += tmp
if k in (ord('q'), ord('e')):
if sc_mode:
ds *= 0.9**tmp
else:
c += tmp
if k == ord('i'):
y, p, r = tools.q_to_ypr(dq)
print('+c: %.3f, %.3f, %.3f' % (x + a, y + b, z + c))
print('ds, h, v: %.3f, %.3f, %.3f' %
(ds, math.degrees(y), math.degrees(r)))
g_ast_q = get_ast_q(x + a, y + b, z + c)
l_ast_q = SystemModel.sc2gl_q.conj() * g_sc_q.conj(
) * g_ast_q * SystemModel.sc2gl_q
def traj_costfun(x,
width,
height,
pp_x,
pp_y,
poses,
pts3d,
pts2d,
cam_idxs,
pt3d_idxs,
meas_r,
meas_aa,
meas_idxs,
obs_kp,
ground_alt,
error_types,
def_fl=None,
def_dist=None,
plot=False):
if def_fl is None and def_dist is None:
fl, dist_coefs = abs(x[0]), x[1:]
elif def_dist is None:
fl, dist_coefs = def_fl, x
else:
fl, dist_coefs = abs(x[0]), def_dist
# run ba
new_poses, new_pts3d, _, _, ba_errs = run_ba(width, height, pp_x, pp_y, fl,
dist_coefs, poses, pts3d,
pts2d, cam_idxs, pt3d_idxs,
meas_r, meas_aa, meas_idxs)
errs = []
labels = []
if ERROR_TYPE_REPROJECTION in error_types:
err_repr = ERROR_COEFS[ERROR_TYPE_REPROJECTION] * np.nanmean(
ba_errs[:, 0])
errs.append(err_repr)
labels.append('repr')
if ERROR_TYPE_LOCATION in error_types:
err_repr = ERROR_COEFS[ERROR_TYPE_LOCATION] * np.nanmean(
np.linalg.norm(ba_errs[:, 1:4], axis=1))
errs.append(err_repr)
labels.append('loc')
if {ERROR_TYPE_ALTITUDE, ERROR_TYPE_CURVATURE,
ERROR_TYPE_DISPERSION}.intersection(error_types):
new_pts3d = new_pts3d[obs_kp, :]
# fit a plane, based on https://math.stackexchange.com/questions/99299/best-fitting-plane-given-a-set-of-points
centroid = np.median(new_pts3d.T, axis=1, keepdims=True)
svd = np.linalg.svd(new_pts3d.T - centroid)
normal = svd[0][:, -1:].T
# fit a parabolic surface to keypoints
x0 = 0, *centroid.flatten(), *normal.flatten()
res = least_squares(paraboloid_costfun,
x0,
verbose=0,
ftol=1e-5,
xtol=1e-5,
gtol=1e-8,
max_nfev=1000,
loss='huber',
f_scale=1.0,
args=(new_pts3d, ))
# extract a plane that corresponds to the fitted parabolic surface (should be more accurate than the median based)
c, x0, y0, z0, xn, yn, zn = res.x
if ERROR_TYPE_CURVATURE in error_types:
errs.append(ERROR_COEFS[ERROR_TYPE_CURVATURE] * c)
labels.append('curv')
if ERROR_TYPE_DISPERSION in error_types:
displ = np.abs(res.fun)
lim = np.quantile(
displ,
0.8) # how well the keypoints lie on the estimated surface
err_disp = np.mean(displ[displ < lim])
errs.append(ERROR_COEFS[ERROR_TYPE_DISPERSION] * err_disp)
labels.append('disp')
if ERROR_TYPE_ALTITUDE in error_types:
centroid = np.array([x0, y0, z0]).reshape((1, 3))
normal = np.array([xn, yn, zn]).reshape((1, 3))
normal /= np.linalg.norm(normal)
pose_cf = new_poses[meas_idxs[-1], :]
loc_wf = (
-Pose(pose_cf[3:], tools.angleaxis_to_q(pose_cf[:3]))).loc
is_takeoff = ERROR_TYPE_CURVATURE in error_types
end_meas_alt = -meas_r[-1 if is_takeoff else 0,
2] - ground_alt # neg z-axis is altitude
if 1:
end_distance = np.abs(
normal.dot(loc_wf[:, None] - centroid.flatten()[:, None]))
err_alt = (end_meas_alt - end_distance)[0, 0]
else:
err_alt = np.quantile(-new_pts3d[:, 2], 0.2) - ground_alt
errs.append(ERROR_COEFS[ERROR_TYPE_ALTITUDE] * err_alt /
end_meas_alt)
labels.append('alt')
if ERROR_TYPE_PITCH in error_types:
ypr = np.array(
[tools.q_to_ypr(tools.angleaxis_to_q(p[:3]))
for p in new_poses]) / np.pi * 180
err_pitch = ypr[-1, 1] - ypr[0, 1]
errs.append(ERROR_COEFS[ERROR_TYPE_PITCH] * err_pitch)
labels.append('pitch')
print('=== fl: %s, dist: %s, cost: %s ===' %
(fl / 0.5, dist_coefs, dict(zip(labels, errs))))
if plot:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(new_pts3d[:, 0], new_pts3d[:, 1], new_pts3d[:, 2], 'C0.')
plt.show()
return np.array(errs)
datetime.fromtimestamp(t0 + t),
prior, quaternion.one)
if res is not None:
tq = res.quat * SystemModel.cv2gl_q
est_q = tq.conj() * res.quat.conj() * tq
err_q = ast_q.conj() * est_q
err_angle = tools.angle_between_q(ast_q, est_q)
est_v = -tools.q_times_v(tq.conj(), res.loc)
err_v = est_v - ast_v
#print('\n')
# print('rea ypr: %s' % ' '.join('%.1fdeg' % math.degrees(a) for a in tools.q_to_ypr(cam_q)))
# print('est ypr: %s' % ' '.join('%.1fdeg' % math.degrees(a) for a in tools.q_to_ypr(res_q)))
print('rea ypr: %s' % ' '.join('%.1fdeg' % math.degrees(a)
for a in tools.q_to_ypr(ast_q)))
print('est ypr: %s' % ' '.join('%.1fdeg' % math.degrees(a)
for a in tools.q_to_ypr(est_q)))
# print('err ypr: %s' % ' '.join('%.2fdeg' % math.degrees(a) for a in tools.q_to_ypr(err_q)))
print('err angle: %.2fdeg' % math.degrees(err_angle))
# print('rea v: %s' % ' '.join('%.1fm' % a for a in cam_v*1000))
# print('est v: %s' % ' '.join('%.1fm' % a for a in res_v*1000))
print('rea v: %s' % ' '.join('%.1fm' % a for a in ast_v * 1000))
print('est v: %s' % ' '.join('%.1fm' % a for a in est_v * 1000))
# print('err v: %s' % ' '.join('%.2fm' % a for a in err_v*1000))
print('err norm: %.2fm\n' % np.linalg.norm(err_v * 1000))
if False and len(odo.state.map3d) > 0:
pts3d = tools.q_times_mx(SystemModel.cv2gl_q.conj(),
odo.get_3d_map_pts())
tools.plot_vectors(pts3d)
def plot_results(keyframes=None,
map3d=None,
frame_names=None,
meta_names=None,
ground_truth=None,
ba_errs=None,
file='result.pickle',
nadir_looking=False):
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
# TODO: remove override
nadir_looking = False
if keyframes is None:
with open(file, 'rb') as fh:
keyframes, map3d, frame_names, meta_names, ground_truth, *ba_errs = pickle.load(
fh)
ba_errs = ba_errs[0] if len(ba_errs) else None
w2b, b2c = NokiaSensor.w2b, NokiaSensor.b2c
nl_dq = tools.eul_to_q(
(-np.pi / 2, ), 'y') if nadir_looking else quaternion.one
est_loc = np.ones((len(keyframes), 3)) * np.nan
est_ori = np.ones((len(keyframes), 3)) * np.nan
meas_loc = np.ones((len(keyframes), 3)) * np.nan
meas_ori = np.ones((len(keyframes), 3)) * np.nan
for i, kf in enumerate(keyframes):
if kf and kf['pose'] and kf['pose'].post:
if kf['pose'].method == VisualOdometry.POSE_2D3D:
est_loc[i, :] = ((
-kf['pose'].post).to_global(b2c).to_global(w2b)).loc
meas_loc[i, :] = ((
-kf['pose'].prior).to_global(b2c).to_global(w2b)).loc
est_ori[i, :] = tools.q_to_ypr(
nl_dq.conj() * ((-kf['pose'].post).to_global(b2c)).quat)
meas_ori[i, :] = tools.q_to_ypr(
nl_dq.conj() * ((-kf['pose'].prior).to_global(b2c)).quat)
est_ori = est_ori / np.pi * 180
meas_ori = meas_ori / np.pi * 180
if nadir_looking:
# TODO: better way, now somehow works heuristically
est_ori = est_ori[:, (2, 0, 1)]
meas_ori = meas_ori[:, (2, 0, 1)]
fst = np.where(np.logical_not(np.isnan(est_loc[:, 0])))[0][0]
idx = np.where([kf['pose'] is not None for kf in keyframes])[0]
idx2 = np.where([
kf['pose'] is not None and kf['meas'] is not None for kf in keyframes
])[0]
t0 = keyframes[idx[0]]['time'].timestamp()
t = np.array([keyframes[i]['time'].timestamp() - t0 for i in idx])
t2 = np.array([
keyframes[i]['time'].timestamp() - t0 + keyframes[i]['meas'].time_off +
keyframes[i]['meas'].time_adj for i in idx2
])
dt = np.array([keyframes[i]['meas'].time_adj for i in idx2])
rng = np.nanmax(est_loc[idx, :2], axis=0) - np.nanmin(est_loc[idx, :2],
axis=0)
rng2 = 0 if len(idx2) == 0 else np.nanmax(
meas_loc[idx2, :2], axis=0) - np.nanmin(meas_loc[idx2, :2], axis=0)
mrg = 0.05 * max(np.max(rng), np.max(rng2))
min_x = min(np.nanmin(est_loc[idx, 0]),
9e9 if len(idx2) == 0 else np.nanmin(meas_loc[idx2, 0])) - mrg
max_x = max(np.nanmax(est_loc[idx, 0]),
-9e9 if len(idx2) == 0 else np.nanmax(meas_loc[idx2, 0])) + mrg
min_y = min(np.nanmin(est_loc[idx, 1]),
9e9 if len(idx2) == 0 else np.nanmin(meas_loc[idx2, 1])) - mrg
max_y = max(np.nanmax(est_loc[idx, 1]),
-9e9 if len(idx2) == 0 else np.nanmax(meas_loc[idx2, 1])) + mrg
min_z = min(np.nanmin(est_loc[idx, 2]),
9e9 if len(idx2) == 0 else np.nanmin(meas_loc[idx2, 2])) - mrg
max_z = max(np.nanmax(est_loc[idx, 2]),
-9e9 if len(idx2) == 0 else np.nanmax(meas_loc[idx2, 2])) + mrg
xx, yy = np.meshgrid(np.linspace(min_x, max_x, 10),
np.linspace(min_y, max_y, 10))
zz = np.ones(xx.shape) * min(min_z + mrg / 2, 0)
min_z = min(0, min_z)
fig = plt.figure(1)
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(xx, yy, zz, zorder=2)
line = ax.plot(est_loc[idx, 0],
est_loc[idx, 1],
est_loc[idx, 2],
'C0',
zorder=3)
ax.scatter(est_loc[fst, 0],
est_loc[fst, 1],
est_loc[fst, 2],
'C2',
zorder=4)
line2 = ax.plot(meas_loc[idx2, 0],
meas_loc[idx2, 1],
meas_loc[idx2, 2],
'C1',
zorder=5)
ax.set_xlim(min_x, max_x)
ax.set_ylim(min_y, max_y)
ax.set_zlim(min_z, max_z)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
tools.hover_annotate(fig, ax, line[0], frame_names)
tools.hover_annotate(fig, ax, line2[0], [meta_names[i] for i in idx2])
if 1:
fig, axs = plt.subplots(1, 1)
axs = [axs]
axs[0].set_aspect('equal')
rng_x = max_x - min_x
rng_y = max_y - min_y
if True or rng_x > rng_y:
axs[0].plot(est_loc[fst, 0], est_loc[fst, 1], 'oC0', mfc='none')
line = axs[0].plot(est_loc[idx, 0], est_loc[idx, 1],
'C0') # , '+-')
line2 = axs[0].plot(meas_loc[idx2, 0], meas_loc[idx2, 1],
'C1') # , '+-')
axs[0].set_xlim(min_x, max_x)
axs[0].set_ylim(min_y, max_y)
axs[0].set_xlabel('x')
axs[0].set_ylabel('y')
else:
axs[0].plot(est_loc[fst, 1], est_loc[fst, 0], 'oC0', mfc='none')
line = axs[0].plot(est_loc[idx, 1], est_loc[idx, 0],
'C0') # , '+-')
line2 = axs[0].plot(meas_loc[idx2, 1], meas_loc[idx2, 0],
'C1') # , '+-')
axs[0].set_xlim(min_y, max_y)
axs[0].set_ylim(min_x, max_x)
axs[0].set_xlabel('y')
axs[0].set_ylabel('x')
tools.hover_annotate(fig, axs[0], line[0], frame_names)
tools.hover_annotate(fig, axs[0], line2[0],
[meta_names[i] for i in idx2])
fig, axs = plt.subplots(2, 1, sharex=True)
axs[0].plot(t[fst], est_loc[fst, 2], 'oC0', mfc='none')
line = axs[0].plot(t, est_loc[idx, 2], 'C0') # , '+-')
line2 = axs[0].plot(t2, meas_loc[idx2, 2], 'C1') # , '+-')
tools.hover_annotate(fig, axs[0], line[0], frame_names)
tools.hover_annotate(fig, axs[0], line2[0],
[meta_names[i] for i in idx2])
axs[0].set_ylabel('z')
axs[0].set_xlabel('t')
if ba_errs is None:
line = axs[1].plot(t2, dt, 'C0') #, '+-')
tools.hover_annotate(fig, axs[1], line[0],
[meta_names[i] for i in idx2])
axs[1].set_ylabel('dt')
else:
id2idx = {kf['id']: i for i, kf in enumerate(keyframes)}
t3 = [
keyframes[id2idx[int(id)]]['time'].timestamp() - t0
for id in ba_errs[:, 0]
]
axs[1].plot(t3, ba_errs[:, 1], 'C0', label='repr [px]')
axs[1].plot(t3, ba_errs[:, 2], 'C1', label='loc [m]')
axs[1].plot(t3, ba_errs[:, 3], 'C2', label='ori [deg]')
axs[1].set_title('BA errors')
axs[1].legend()
axs[1].set_xlabel('t')
fig, axs = plt.subplots(3, 1, sharex=True)
for i, title in enumerate(('yaw', 'pitch', 'roll')):
axs[i].plot(t[fst], est_ori[fst, i], 'oC0', mfc='none')
line = axs[i].plot(t, est_ori[idx, i], 'C0') # , '+-')
line2 = axs[i].plot(t2, meas_ori[idx2, i], 'C1') # , '+-')
tools.hover_annotate(fig, axs[i], line[0], frame_names)
tools.hover_annotate(fig, axs[i], line2[0],
[meta_names[j] for j in idx2])
axs[i].set_ylabel(title)
axs[i].set_xlabel('t')
plt.tight_layout()
if map3d is not None and len(map3d) > 0:
x, y, z = np.array(
[tools.q_times_v(w2b.quat * b2c.quat, pt.pt3d) for pt in map3d]).T
if 1:
fig, ax = plt.subplots(1, 1)
ax.set_aspect('equal')
ax.set_xlabel("east", fontsize=12)
ax.set_ylabel("north", fontsize=12)
line = ax.scatter(x,
y,
s=20,
c=z,
marker='o',
vmin=-5.,
vmax=100.,
cmap=cm.get_cmap('jet')) #, vmax=20.)
fig.colorbar(line)
tools.hover_annotate(fig, ax, line, ['%.1f' % v for v in z])
else:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(x, y, z, '.')
ax.set_xlabel("east", fontsize=12)
ax.set_ylabel("north", fontsize=12)
ax.set_zlabel("alt", fontsize=12)
plt.show()
print('ok: %.1f%%, delta loc std: %.3e' % (
100 * (1 - np.mean(np.isnan(est_loc[:, 0]))),
np.nanstd(np.linalg.norm(np.diff(est_loc[:, :3], axis=0), axis=1)),
))
def main():
# parse arguments
parser = argparse.ArgumentParser(
description='Run visual odometry on a set of images')
parser.add_argument('--data', '-d', metavar='DATA', help='path to data')
parser.add_argument('--meta',
'-t',
metavar='META',
help='path to meta data')
parser.add_argument('--video-toff',
'--dt',
type=float,
metavar='dT',
help='video time offset compared to metadata')
parser.add_argument('--alt-scale',
'--as',
type=float,
default=1.0,
metavar='sc',
help='scale telemetry altitude with this value')
parser.add_argument('--res',
'-r',
metavar='OUT',
help='path to the result pickle')
parser.add_argument('--debug-out',
'-o',
metavar='OUT',
help='path to the debug output folder')
parser.add_argument('--kapture',
metavar='OUT',
help='path to kapture-format export folder')
parser.add_argument(
'--verbosity',
'-v',
type=int,
default=2,
help=
'verbosity level (0-4, 0-1: text only, 2:+debug imgs, 3: +keypoints, 4: +poses)'
)
parser.add_argument(
'--high-quality',
action='store_true',
help='high quality settings with more keypoints detected')
parser.add_argument('--mission',
'-mr',
choices=('hwproto', 'nokia', 'toynokia'),
help='select mission')
parser.add_argument('--undist-img',
action='store_true',
help='undistort image instead of keypoints')
parser.add_argument('--use-gimbal',
action='store_true',
help='gimbal data is ok, use it')
parser.add_argument(
'--drifting-gimbal',
action='store_true',
help=
'gimbal orientation measure drifts, update orientation offset with each odometry result'
)
parser.add_argument('--nadir-looking',
action='store_true',
help='downwards looking cam')
parser.add_argument(
'--ori-off-ypr',
type=float,
nargs=3,
help=
'orientation offset in ypr (deg) to be applied in body frame to orientation measurements'
)
parser.add_argument('--cam-dist',
type=float,
nargs='*',
help='cam distortion coeffs')
parser.add_argument('--cam-fl-x', type=float, help='cam focal length x')
parser.add_argument('--cam-fl-y', type=float, help='cam focal length y')
parser.add_argument('--cam-pp-x', type=float, help='cam principal point x')
parser.add_argument('--cam-pp-y', type=float, help='cam principal point y')
parser.add_argument('--first-frame',
'-f',
type=int,
default=0,
help='first frame (default: 0; -1: hardcoded value)')
parser.add_argument('--last-frame',
'-l',
type=int,
help='last frame (default: None; -1: hardcoded end)')
parser.add_argument('--skip',
'-s',
type=int,
default=1,
help='use only every xth frame (default: 1)')
parser.add_argument('--plot-only',
action='store_true',
help='only plot the result')
args = parser.parse_args()
if args.verbosity > 1:
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
# init odometry and data
if args.mission == 'hwproto':
mission = HardwarePrototype(args.data, last_frame=(155, 321, None)[0])
elif args.mission == 'nokia':
ff = (100, 250, 520, 750, 1250, 1500,
1750)[0] if args.first_frame == -1 else args.first_frame
lf = (240, 400, 640, 1000, 1500, 1750,
2000)[1] if args.last_frame == -1 else args.last_frame
cam_mx = None
if args.cam_fl_x:
fl_y = args.cam_fl_y or args.cam_fl_x
pp_x = args.cam_pp_x or NokiaSensor.CAM_WIDTH / 2
pp_y = args.cam_pp_y or NokiaSensor.CAM_HEIGHT / 2
cam_mx = [[args.cam_fl_x, 0., pp_x], [0., fl_y, pp_y],
[0., 0., 1.]]
mission = NokiaSensor(
args.data,
data_path=args.meta,
video_toff=args.video_toff,
use_gimbal=args.use_gimbal,
undist_img=args.undist_img,
cam_mx=cam_mx,
cam_dist=args.cam_dist,
verbosity=args.verbosity,
high_quality=args.high_quality,
alt_scale=args.alt_scale,
ori_off_q=tools.ypr_to_q(*map(math.radians, args.ori_off_ypr))
if args.ori_off_ypr else None,
first_frame=ff,
last_frame=lf)
elif args.mission == 'toynokia':
mission = ToyNokiaSensor(args.data,
data_path=args.meta,
video_toff=args.video_toff,
first_frame=(100, 350, 850, 1650)[1],
last_frame=(415, 500, 1250, 1850, 2000)[2])
else:
assert False, 'bad mission given: %s' % args.mission
if args.debug_out:
mission.odo._track_save_path = args.debug_out
if args.nadir_looking:
mission.odo._nadir_looking = True
res_file = args.res or ('%s-result.pickle' % args.mission)
if args.plot_only:
plot_results(file=res_file, nadir_looking=args.nadir_looking)
replay_keyframes(mission.odo.cam, file=res_file)
return
# run odometry
prior = Pose(np.array([0, 0, 0]), quaternion.one)
frame_names0 = []
meta_names0 = []
results0 = []
ground_truth0 = []
ori_offs = []
ba_errs = []
kfid2img = {}
started = datetime.now()
ax = None
def ba_err_logger(frame_id, per_frame_ba_errs):
per_frame_ba_errs = np.stack(
(per_frame_ba_errs[:, 0],
np.linalg.norm(per_frame_ba_errs[:, 1:4], axis=1),
np.linalg.norm(per_frame_ba_errs[:, 4:7], axis=1) / np.pi * 180),
axis=1)
ba_errs.append([frame_id, *np.nanmean(per_frame_ba_errs, axis=0)])
mission.odo.ba_err_logger = ba_err_logger
vid_id = re.search(r'(^|\\|/)([\w-]+)\.mp4$', mission.video_path)
for i, (img, t, name, meta, meta_name, gt) in enumerate(mission.data):
if i % args.skip != 0:
continue
logger.info('')
logger.info(name)
frame_names0.append(name)
meta_names0.append(meta_name)
ground_truth0.append(gt)
if vid_id[2] == 'HD_CAM-1__514659341_03_12_2020_16_12_44' and t > 1050:
# hardcoding just to get dataset 10 to work
mission.odo.ori_off_q = tools.ypr_to_q(math.radians(0), 0, 0)
try:
nf, *_ = mission.odo.process(img,
datetime.fromtimestamp(mission.time0 +
t),
measure=meta)
if nf is not None and nf.id is not None:
kfid2img[nf.id] = i
if args.drifting_gimbal and nf.pose.post and meta:
est_w2c_bf_q = (-nf.pose.post).to_global(mission.b2c).quat
meas_w2c_bf_q = (-nf.pose.prior).to_global(
mission.b2c).quat * mission.odo.ori_off_q.conj()
new_ori_off_q = meas_w2c_bf_q.conj() * est_w2c_bf_q
filtered_ori_off_q = tools.mean_q(
[mission.odo.ori_off_q, new_ori_off_q], [0.8, 0.2])
if 0:
# TODO: debug, something wrong here as roll and pitch explode
mission.odo.ori_off_q = filtered_ori_off_q
else:
y, p, r = tools.q_to_ypr(filtered_ori_off_q)
mission.odo.ori_off_q = tools.ypr_to_q(y, 0, 0)
ori_offs.append(mission.odo.ori_off_q)
logger.info('new gimbal offset: %s' % (list(
map(lambda x: round(math.degrees(x), 2),
tools.q_to_ypr(mission.odo.ori_off_q))), ))
pts3d = np.array([
pt.pt3d for pt in mission.odo.state.map3d.values()
if pt.active
])
if len(pts3d) > 0 and nf.pose.post:
ground_alt = np.quantile(
-pts3d[:, 1], 0.2) # neg-y is altitude in cam frame
drone_alt = -(-nf.pose.post).loc[1]
expected_dist = drone_alt - mission.coord0[2]
modeled_dist = drone_alt - ground_alt
logger.info(
'ground at %.1f mr (%.1f mr), drone alt %.1f mr (%.1f mr)'
% (ground_alt, mission.coord0[2], modeled_dist,
expected_dist))
if args.verbosity > 3:
keyframes = mission.odo.all_keyframes()
post = np.zeros((len(keyframes), 7))
k, prior = 0, np.zeros((len(keyframes), 7))
for j, kf in enumerate(
[kf for kf in keyframes if kf.pose.post]):
post[j, :] = (-kf.pose.post).to_global(
NokiaSensor.b2c).to_global(
NokiaSensor.w2b).to_array()
if kf.measure is not None:
prior[k, :] = (-kf.pose.prior).to_global(
NokiaSensor.b2c).to_global(
NokiaSensor.w2b).to_array()
k += 1
if ax is not None:
ax.clear()
ax = tools.plot_poses(post,
axis=(0, 1, 0),
up=(0, 0, 1),
ax=ax,
wait=False)
tools.plot_poses(prior[:k, :],
axis=(0, 1, 0),
up=(0, 0, 1),
ax=ax,
wait=False,
colors=map(lambda c: (c, 0, 0, 0.5),
np.linspace(.3, 1.0, k)))
plt.pause(0.05)
except TypeError as e:
if 0:
nf, *_ = mission.odo.process(
img, datetime.fromtimestamp(mission.time0 + t), prior,
quaternion.one)
if nf and nf.pose.post:
prior = nf.pose.post
else:
raise e
results0.append(None if nf is None or nf.pose is None
or nf.pose.post is None else (
nf.pose, getattr(nf, 'measure', None), nf.time,
nf.id))
try:
mission.odo.flush_state() # flush all keyframes and keypoints
map3d = mission.odo.removed_keypoints
# results = [(kf.pose, kf.measure, kf.time, kf.id) for kf in mission.odo.removed_keyframes if kf.pose.post]
keyframes = [
dict(pose=kf.pose,
meas=kf.measure,
time=kf.time,
id=kf.id,
kps_uv=kf.kps_uv) for kf in mission.odo.removed_keyframes
if kf.pose.post
]
frame_names = [frame_names0[kfid2img[kf['id']]] for kf in keyframes]
meta_names = [meta_names0[kfid2img[kf['id']]] for kf in keyframes]
ground_truth = [ground_truth0[kfid2img[kf['id']]] for kf in keyframes]
ba_errs = np.array(ba_errs)
if 1:
pts3d = np.array([pt.pt3d for pt in map3d])
ground_alt = np.quantile(-pts3d[:, 1],
0.5) # neg-y is altitude in cam frame
drone_alt = -(-keyframes[-1]['pose'].post).loc[1] # word
expected_dist = drone_alt - mission.coord0[2]
modeled_dist = drone_alt - ground_alt
logger.info(
'ground at %.1f m (%.1f m), drone alt %.1f m (%.1f m)' %
(ground_alt, mission.coord0[2], modeled_dist, expected_dist))
if args.kapture:
kapture = KaptureIO(args.kapture,
reset=True,
jpg_qlt=95,
scale=0.5)
kapture.set_camera(1, 'cam', mission.cam)
kapture.add_frames(mission.odo.removed_keyframes, map3d)
kapture.write_to_dir()
except AttributeError as e:
if 0:
map3d = None
results = [(kf.pose, None, kf.time, kf.id)
for kf in mission.odo.all_keyframes()]
else:
raise e
mission.odo.quit()
if 0:
# show results as given by the online version of the algorithm
results, frame_names, meta_names, ground_truth = results0, frame_names0, meta_names0, ground_truth0
logger.info('time spent: %.0fs' %
(datetime.now() - started).total_seconds())
repr_errs = np.concatenate([
list(kf.repr_err.values()) for kf in mission.odo.removed_keyframes
if len(kf.repr_err)
])
err_q95 = np.quantile(np.linalg.norm(repr_errs, axis=1),
0.95) if len(repr_errs) else np.nan
logger.info('95%% percentile repr err: %.3fpx' % (err_q95, ))
interp = interp_loc(mission.odo.removed_keyframes, mission.time0)
loc_est = np.array([
np.ones((3, )) * np.nan if kf.pose.post is None else
(-kf.pose.post).loc for kf in mission.odo.removed_keyframes
])
loc_gps = np.array([
interp(kf.time.timestamp() - mission.time0).flatten()
for kf in mission.odo.removed_keyframes
]).squeeze()
mean_loc_err = np.nanmean(np.linalg.norm(loc_est - loc_gps, axis=1))
logger.info('mean loc err: %.3fm' % (mean_loc_err, ))
logger.info('mean ba errs (repr, loc, ori): %.3fpx %.3fm %.3fdeg' %
(*np.nanmean(ba_errs[:, 1:], axis=0), ))
logger.info('latest ba errs (repr, loc, ori): %.3fpx %.3fm %.3fdeg' %
(*ba_errs[-1, 1:], ))
if 0:
plt.figure(10)
plt.plot(loc_est[:, 0], loc_est[:, 1])
plt.plot(loc_gps[:, 0], loc_gps[:, 1])
plt.show()
if args.verbosity > 3:
plt.show() # stop to show last trajectory plot
res_folder = os.path.dirname(res_file)
if res_folder:
os.makedirs(res_folder, exist_ok=True)
with open(res_file, 'wb') as fh:
pickle.dump(
(keyframes, map3d, frame_names, meta_names, ground_truth, ba_errs),
fh)
if args.verbosity > 1:
plot_results(keyframes,
map3d,
frame_names,
meta_names,
ground_truth,
ba_errs,
res_file,
nadir_looking=args.nadir_looking)
def flux_density(cam_q,
cam,
mask=None,
mag_cutoff=MAG_CUTOFF,
array=False,
undistorted=False,
order_by=None):
"""
plots stars based on Tycho-2 database, gives out photon count per unit area given exposure time in seconds,
cam_q is a quaternion in ICRS coord frame, x_fov and y_fov in degrees
"""
# calculate query conditions for star ra and dec
cam_dec, cam_ra, _ = tools.q_to_ypr(
cam_q) # camera boresight in ICRS coords
d = np.linalg.norm((cam.x_fov, cam.y_fov)) / 2
min_dec, max_dec = math.degrees(cam_dec) - d, math.degrees(cam_dec) + d
dec_cond = '(dec BETWEEN %s AND %s)' % (min_dec, max_dec)
# goes over the pole to the other side of the sphere, easy solution => ignore limit on ra
skip_ra_cond = min_dec < -90 or max_dec > 90
if skip_ra_cond:
ra_cond = '1'
else:
min_ra, max_ra = math.degrees(cam_ra) - d, math.degrees(cam_ra) + d
if min_ra < 0:
ra_cond = '(ra < %s OR ra > %s)' % (max_ra,
(min_ra + 360) % 360)
elif max_ra > 360:
ra_cond = '(ra > %s OR ra < %s)' % (min_ra, max_ra % 360)
else:
ra_cond = '(ra BETWEEN %s AND %s)' % (min_ra, max_ra)
conn = sqlite3.connect(Stars.STARDB)
cursor = conn.cursor()
# the magnitudes for tycho id xxxx-xxxxx-2 entries are bad as they are most likely taken from hip catalog that bundles all .*-(\d)
results = cursor.execute(
"""
SELECT x, y, z, mag_v""" +
(", mag_b, t_eff, fe_h, log_g, dec, ra, id" if array else "") + """
FROM deep_sky_objects
WHERE """ + ("tycho like '%-1' AND " if Stars.STARDB ==
Stars.STARDB_TYC else "") + "mag_v < " +
str(mag_cutoff) + " AND " + dec_cond + " AND " + ra_cond +
((" ORDER BY %s ASC" % order_by) if order_by is not None else ''))
stars = np.array(results.fetchall())
conn.close()
flux_density = ([], None) if array else np.zeros(
(cam.height, cam.width), dtype=np.float32)
if len(stars) == 0:
return flux_density
stars[:, 0:3] = tools.q_times_mx(
SystemModel.sc2gl_q.conj() * cam_q.conj(), stars[:, 0:3])
stars_ixy_ = cam.calc_img_R(stars[:, 0:3], undistorted=undistorted)
stars_ixy = np.round(stars_ixy_.astype(np.float)).astype(np.int)
I = np.logical_and.reduce(
(np.all(stars_ixy >= 0, axis=1), stars_ixy[:, 0] <= cam.width - 1,
stars_ixy[:, 1] <= cam.height - 1))
if array:
cols = ('ix', 'iy', 'x', 'y', 'z', 'mag_v', 'mag_b', 't_eff',
'fe_h', 'log_g', 'dec', 'ra', 'id')
return (np.hstack((stars_ixy_[I, :], stars[I, :])),
dict(zip(cols, range(len(cols)))))
stars_ixy = stars_ixy[I, :]
flux_density_per_star = Stars.magnitude_to_spectral_flux_density(
stars[I, 3])
for i, f in enumerate(flux_density_per_star):
flux_density[stars_ixy[i, 1], stars_ixy[i, 0]] += f
if mask is not None:
flux_density[np.logical_not(mask)] = 0
if True:
# assume every star is like our sun, convert to total flux density [W/m2]
solar_constant = 1360.8
# sun magnitude from http://mips.as.arizona.edu/~cnaw/sun.html
sun_flux_density = Stars.magnitude_to_spectral_flux_density(
Stars.SUN_MAG_V)
flux_density = flux_density * (solar_constant / sun_flux_density)
return flux_density
def _match_stars(self,
stars,
max_dist=0.05,
max_mag_diff=2.0,
mag_cutoff=3.0,
plot=False):
""" match stars based on proximity """
merge_lim = 4
all_stars, cols = Stars.flux_density(self.q,
self.cam[0],
array=True,
undistorted=True,
mag_cutoff=mag_cutoff + merge_lim,
order_by='mag_v')
if self.debug:
db_img = np.sqrt(
Stars.flux_density(self.q, self.cam[0], mag_cutoff=10.0))
# override some star data, change None => nan
for i, st in enumerate(all_stars):
for j in range(len(st)):
st[j] = np.nan if st[j] is None else st[j]
if st[cols['id']] in self.override_star_data:
for f in ('mag_v', 'mag_b', 't_eff', 'log_g', 'fe_h'):
od = self.override_star_data[st[cols['id']]]
if f in od:
all_stars[i][cols[f]] = od[f]
# merge close stars
all_stars = np.array(all_stars)
points = np.array([(s[cols['ix']], s[cols['iy']]) for s in all_stars])
D = tools.distance_mx(points, points)
radius = 10 if self.cam[0].width > 300 else 2
db_stars = []
added = set()
for i, s in enumerate(all_stars):
if i in added:
continue
I = tuple(
set(
np.where(
np.logical_and(
D[i, :] < radius, all_stars[:, cols['mag_v']] -
merge_lim < s[cols['mag_v']]))[0]) - added)
cluster = [None] * (max(cols.values()) + 1)
cluster[cols['id']] = tuple(all_stars[I,
cols['id']].astype(np.int))
amag_v = 10**(-all_stars[I, cols['mag_v']] / 2.5)
amag_b = 10**(-all_stars[I, cols['mag_b']] / 2.5)
cluster[cols['mag_v']] = -2.5 * np.log10(np.sum(amag_v))
cluster[cols['mag_b']] = -2.5 * np.log10(np.sum(amag_b))
for c in ('ix', 'iy', 'dec', 'ra', 't_eff', 'fe_h', 'log_g'):
E = np.where(all_stars[I, cols[c]] != None)[0]
cluster[cols[c]] = np.sum(amag_v[E] *
all_stars[I, cols[c]][E]) / np.sum(
amag_v[E]) if len(E) else None
if cluster[cols['mag_v']] < mag_cutoff:
added.update(I)
db_stars.append(cluster)
img_st = np.array([(s['x'], s['y'], s['mag'], s['size'])
for s in stars])
db_st = np.array([(s[cols['ix']], s[cols['iy']], s[cols['mag_v']])
for s in db_stars])
# adjust mags to match, not easy to make match directly as unknown variable black level removed in image sensor
#b0, b1 = np.min(img_st[:, 2]), np.min(db_st[:, 2])
#d0, d1 = np.max(img_st[:, 2]), np.max(db_st[:, 2])
#img_st[:, 2] = (img_st[:, 2] - b0) * (d1-b1)/(d0-b0) + b1
#img_st[:, 2] = np.log10((10**img_st[:, 2] - 10**b0) * (10**d1-10**b1)/(10**d0-10**b0) + 10**b1)
img_st[:, 2] = img_st[:, 2] - np.median(img_st[:, 2]) + np.median(
db_st[:, 2])
if self.cam[0].dist_coefs is not None:
db_st[:, :2] = Camera.distort(
db_st[:, :2], self.cam[0].dist_coefs,
self.cam[0].intrinsic_camera_mx(legacy=False),
self.cam[0].inv_intrinsic_camera_mx(legacy=False))
M = (np.abs(
np.repeat(
np.expand_dims(img_st[:, 2:3], axis=0), len(db_st), axis=0) -
np.repeat(
np.expand_dims(db_st[:, 2:3], axis=1), len(img_st), axis=1))
).squeeze()
D = np.repeat(np.expand_dims(img_st[:, :2], axis=0), len(db_st), axis=0) \
- np.repeat(np.expand_dims(db_st[:, :2], axis=1), len(img_st), axis=1)
D = np.sum(D**2, axis=2)
D = D.flatten()
D[M.flatten() > max_mag_diff] = np.inf
D = D.reshape(M.shape)
idxs = np.argmin(D, axis=0)
max_dist = (self.image.shape[1] * max_dist)**2
m_idxs = {}
for i1, j in enumerate(idxs):
dist = D[j, i1]
if dist > max_dist or j in m_idxs and dist > D[j, m_idxs[j]]:
# discard match if distance too high or better match for same db star available
continue
m_idxs[j] = i1
matches = [None] * len(idxs)
for j, i in m_idxs.items():
matches[i] = db_stars[j]
if self.debug and DEBUG_MATCHING or plot:
if plot:
norm = ImageNormalize(stretch=SqrtStretch())
size = np.median(img_st[:, 3].astype('int'))
data = np.mean(self.image.astype(np.float64) /
(2**self.bits - 1),
axis=2)
ud_I = set(range(len(db_st))) - set(m_idxs.keys())
d_I = set(range(len(img_st))) - set(m_idxs.values())
# detected_pos = img_st[tuple(d_I), :2].astype('int')
# matched_pos = img_st[tuple(m_idxs.values()), :2].astype('int')
# undetected_pos = db_st[tuple(ud_I), :2].astype('int')
plt.imshow(data, cmap='Greys', norm=norm)
for i in d_I: # detected
CircularAperture(img_st[i, :2].astype('int'),
r=img_st[i, 3].astype('int')).plot(
color='blue', lw=1.5, alpha=0.5)
for i in m_idxs.values(): # matched
CircularAperture(img_st[i, :2].astype('int'),
r=img_st[i, 3].astype('int')).plot(
color='green', lw=1.5, alpha=0.5)
for i in ud_I: # undetected
CircularAperture(db_st[i, :2].astype('int'),
r=size).plot(color='red',
lw=1.5,
alpha=0.5)
plt.show()
else:
dec, ra, pa = map(math.degrees, tools.q_to_ypr(self.q))
print('ra: %.1f, dec: %.1f, pa: %.1f' % (ra, dec, pa))
sc, isc = 1, (1024 if 0 else 2800) / (self.image.shape[1] * 2)
img = np.sqrt(self.image)
img = ((img / np.max(img)) * 255).astype('uint8')
img = cv2.resize(img,
None,
fx=isc,
fy=isc,
interpolation=cv2.INTER_AREA)
cv2.drawKeypoints(img, [
cv2.KeyPoint(x * isc, y * isc, 60 * sc)
for x, y in db_st[:, :2]
], img, [0, 255, 0],
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.drawKeypoints(img, [
cv2.KeyPoint(x * isc, y * isc, 60 * sc)
for x, y in img_st[:, :2]
], img, [255, 0, 0],
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
for j, i in m_idxs.items():
cv2.line(
img,
tuple(np.round(img_st[i, :2] * isc).astype('int')),
tuple(np.round(db_st[j, :2] * isc).astype('int')),
[0, 255, 0])
if DEBUG_MATCHING != 3:
for j, pt in enumerate(db_st[:, :2]):
cv2.putText(img, str(j),
tuple(np.round(pt * isc).astype('int')),
cv2.FONT_HERSHEY_SIMPLEX, 3 * sc,
[0, 255, 0])
else:
for i, pt in enumerate(img_st[:, :2]):
text = '%.2f' % img_st[i, 2]
#text = str(i)
cv2.putText(img, text,
tuple(np.round(pt * isc).astype('int')),
cv2.FONT_HERSHEY_SIMPLEX, 1.6 * sc,
[0, 0, 255])
db_img = np.repeat(np.expand_dims(db_img, axis=2),
self.image.shape[2],
axis=2)
db_img = ((db_img / np.max(db_img)) * 255).astype('uint8')
db_img = cv2.resize(db_img,
None,
fx=isc,
fy=isc,
interpolation=cv2.INTER_AREA)
for j, pt in enumerate(db_st[:, :2]):
if DEBUG_MATCHING == 1:
text = '&'.join([
Stars.get_catalog_id(id)
for id in db_stars[j][cols['id']]
])
elif DEBUG_MATCHING == 2:
t_eff = db_stars[j][cols['t_eff']]
t_eff2 = Stars.effective_temp(
db_stars[j][cols['mag_b']] -
db_stars[j][cols['mag_v']])
#if 1:
# print('%s Teff: %s (%.1f)' % (Stars.get_catalog_id(db_stars[j][cols['id']]), t_eff, t_eff2))
text = ('%dK' % t_eff) if t_eff else ('(%dK)' % t_eff2)
elif DEBUG_MATCHING == 3:
text = '%.2f' % db_stars[j][cols['mag_v']]
cv2.putText(
db_img, text,
tuple(
np.round(pt * isc +
np.array([5, -5])).astype('int')),
cv2.FONT_HERSHEY_SIMPLEX, 1.6 * sc, [255, 0, 0])
cv2.drawKeypoints(db_img, [
cv2.KeyPoint(x * isc, y * isc, 60 * sc)
for x, y in db_st[:, :2]
], db_img, [0, 255, 0],
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
img = np.hstack(
(db_img,
np.ones(
(img.shape[0], 1, img.shape[2]), dtype='uint8') * 255,
img))
cv2.imshow('test', img)
cv2.waitKey()
# plt.figure(1, (16, 12))
# plt.imshow(np.flip(img, axis=2))
# plt.tight_layout()
# plt.show()
return matches, cols
