def f_IMU(self, X, dt, dt2, accel, ori, isMoving, noise):
""" Transition model
- 状態方程式
x_t = f(x_t-1, u) + w
w ~ N(0, sigma)
"""
if (isMoving[0] == False):
X.v[0] = 0.0
if (isMoving[1] == False):
X.v[1] = 0.0
if (isMoving[2] == False):
X.v[2] = 0.0
X_new = Particle()
X_new.landmarks = X.landmarks
# Transition with noise (only x,v)
X_new.x = X.x + dt * X.v + dt2 * X.a + dt2 * noise
X_new.v = X.v + dt * X.a + dt * noise
X_new.a = accel
X_new.o = ori
#if(isMoving[0] == False):
# X_new.v[0] = 0.0
#if(isMoving[1] == False):
# X_new.v[1] = 0.0
#if(isMoving[2] == False):
# X_new.v[2] = 0.0
return X_new
def predictionAndUpdateOneParticle_firsttime(self, X, dt, dt2, keypoints, step, P):
"""
FastSLAM1.0
"""
weight = 0.0 # weight (return value)
weights = []
# 姿勢予測 prediction of position
X_ = Particle()
X_.landmarks = X.landmarks
X_.x = X.x + dt*X.v + dt2*X.a
X_.v = X.v + dt*X.a
X_.a = X.a
X_.o = X.o
for keypoint in keypoints:
# previous landmark id
prevLandmarkId = (step-1)*10000 + keypoint.prevIndex
# new landmark id
landmarkId = step*10000 + keypoint.index
# The landmark is already observed or not?
if(X_.landmarks.has_key(prevLandmarkId) == False):
# Fisrt observation
# Initialize landmark and append to particle
landmark = Landmark()
landmark.init(X_, keypoint, P, self.focus)
X_.landmarks[landmarkId] = landmark
else:
print("Error on pf_step_camera_firsttime")
self.count+=1
return X_, weight
def predictionAndUpdateOneParticle_firsttime(self, X, dt, dt2, keypoints,
step, P):
"""
FastSLAM1.0
"""
weight = 0.0 # weight (return value)
weights = []
# 姿勢予測 prediction of position
X_ = Particle()
X_.landmarks = X.landmarks
X_.x = X.x + dt * X.v + dt2 * X.a
X_.v = X.v + dt * X.a
X_.a = X.a
X_.o = X.o
for keypoint in keypoints:
# previous landmark id
prevLandmarkId = (step - 1) * 10000 + keypoint.prevIndex
# new landmark id
landmarkId = step * 10000 + keypoint.index
# The landmark is already observed or not?
if (X_.landmarks.has_key(prevLandmarkId) == False):
# Fisrt observation
# Initialize landmark and append to particle
landmark = Landmark()
landmark.init(X_, keypoint, P, self.focus)
X_.landmarks[landmarkId] = landmark
else:
print("Error on pf_step_camera_firsttime")
self.count += 1
return X_, weight
def f_IMU(self, X, dt, dt2, accel, ori, noise): """ Transition model - 状態方程式 x_t = f(x_t-1, u) + w w ~ N(0, sigma) """ X_new = Particle() X_new.landmarks = X.landmarks # Transition with noise (only x,v) X_new.x = X.x + dt*X.v + dt2*X.a + noise X_new.v = X.v + dt*X.a #X_new.v = X.v + dt*X.a + noise*25 #X_new.v = X.v + dt*X.a + noise*50 X_new.a = accel X_new.o = ori return X_new
def f_IMU(self, X, dt, dt2, accel, ori, noise):
""" Transition model
- 状態方程式
x_t = f(x_t-1, u) + w
w ~ N(0, sigma)
"""
X_new = Particle()
X_new.landmarks = X.landmarks
# Transition with noise (only x,v)
X_new.x = X.x + dt * X.v + dt2 * X.a + noise
X_new.v = X.v + dt * X.a
#X_new.v = X.v + dt*X.a + noise*25
#X_new.v = X.v + dt*X.a + noise*50
X_new.a = accel
X_new.o = ori
return X_new
def predictionAndUpdateOneParticle(self, X, dt, dt2, noise, keypoints, step, P, X1, P1, dt_camera, noise_o):
"""
FastSLAM1.0
"""
weight = 0.0 # weight (return value)
weights = []
count_of_first_observation = 0
# 姿勢予測 prediction of position
X_ = Particle()
X_.landmarks = X.landmarks
X_.x = X.x + dt*X.v + dt2*X.a + noise
#X_.v = X.v + dt*X.a
#X_.v = X.v + dt*X.a + noise*25
#X_.v = X.v + dt*X.a + noise*50
X_.a = X.a
X_.o = X.o
#X_.o = X.o + noise_o
# 速度調整 velocity adjustment
#X_.v = ((X_.x - X1.x)/dt_camera)
X_.v = ((X_.x - X1.x)/dt_camera)*0.25
#X_.v = ((X_.x - X1.x)/dt_camera)*0.5
# 共面条件モデルのための計算 Calc for Coplanarity
xyz = np.array([X_.x[0] - X1.x[0], X_.x[1] - X1.x[1], X_.x[2] - X1.x[2]])
# 前回ランドマークの全てのキーを取得しておく あとで消す
prevLandmarkKeys = []
for key in X_.landmarks:
prevLandmarkKeys.append(key)
for keypoint in keypoints:
# ---------------------------- #
# Calc weight of Inverse Depth #
# ---------------------------- #
# previous landmark id
prevLandmarkId = (step-1)*10000 + keypoint.prevIndex
# new landmark id
landmarkId = step*10000 + keypoint.index
# The landmark is already observed or not?
if(X_.landmarks.has_key(prevLandmarkId) == False):
# Fisrt observation
# Initialize landmark and append to particle
landmark = Landmark()
landmark.initPrev(X1, keypoint, P1, self.focus)
X_.landmarks[landmarkId] = landmark
if(self.count == 0):
count_of_first_observation += 1
else:
# Already observed
X_.landmarks[landmarkId] = X_.landmarks[prevLandmarkId]
#del X_.landmarks[prevLandmarkId]
# Update landmark and calc weight
# Observation z
z = np.array([keypoint.x, keypoint.y])
# Calc h and H (Jacobian matrix of h)
h, Hx, H = X_.landmarks[landmarkId].calcObservation(X_, self.focus)
# Kalman filter (Landmark update)
S = H.dot(X_.landmarks[landmarkId].sigma.dot(H.T)) + self.R
Sinv = np.linalg.inv(S)
K = X_.landmarks[landmarkId].sigma.dot(H.T.dot(Sinv))
X_.landmarks[landmarkId].mu += K.dot(z - h)
X_.landmarks[landmarkId].sigma = X_.landmarks[landmarkId].sigma - K.dot(H.dot(X_.landmarks[landmarkId].sigma))
# Calc weight
w = 0.0
try:
#w = (1.0 / (math.sqrt( np.linalg.det(2.0 * math.pi * S) ))) * np.exp( -0.5 * ( (z-h).T.dot(Sinv.dot(z-h)) ) )
#w = (1.0 / (math.sqrt( np.linalg.det(2.0 * math.pi * self.R) ))) * np.exp( -0.5 * ( (z-h).T.dot(self.Rinv.dot(z-h)) ) )
# log likelihood
#w = np.log(1.0 / (math.sqrt( np.linalg.det(2.0 * math.pi * self.R) ))) + ( -0.5 * ( (z-h).T.dot(self.Rinv.dot(z-h)) ) )
w = ( -0.5 * ( (z-h).T.dot(self.Rinv.dot(z-h)) ) )
except:
print("Error on calc inverse weight ******")
w = 0.0
# -------------------------- #
# Calc weight of Coplanarity #
# -------------------------- #
# Generate uvw1 (time:t-1)
uvf1 = np.array([keypoint.x1, -keypoint.y1, -self.focus]) # Camera coordinates -> Device coordinates
rotX = Util.rotationMatrixX(X1.o[0])
rotY = Util.rotationMatrixY(X1.o[1])
rotZ = Util.rotationMatrixZ(X1.o[2])
# uvw1 = R(z)R(y)R(x)uvf1
uvw1 = np.dot(rotZ,np.dot(rotY,np.dot(rotX,uvf1)))
uvw1 /= 100.0 # Adjust scale to decrease calculation error. This doesn't have an influence to estimation.
# Generate uvw2 (time:t)
uvf2 = np.array([keypoint.x, -keypoint.y, -self.focus]) # Camera coordinates -> Device coordinates
rotX = Util.rotationMatrixX(X_.o[0])
rotY = Util.rotationMatrixY(X_.o[1])
rotZ = Util.rotationMatrixZ(X_.o[2])
# uvw2 = R(z)R(y)R(x)uvf2
uvw2 = np.dot(rotZ,np.dot(rotY,np.dot(rotX,uvf2)))
uvw2 /= 100.0 # Adjust scale to decrease calculation error. This doesn't have an influence to estimation.
# Generate coplanarity matrix
coplanarity_matrix = np.array([xyz,uvw1,uvw2])
# Calc determinant
determinant = np.linalg.det(coplanarity_matrix)
# Weight
w_coplanarity = 0.0
try:
#w_coplanarity = (1.0 / (math.sqrt( 2.0 * math.pi * self.noise_coplanarity**2 ))) * np.exp((determinant**2) / (-2.0 * (self.noise_coplanarity**2)) )
# log likelihood
#w_coplanarity = np.log(1.0 / (math.sqrt( 2.0 * math.pi * self.noise_coplanarity**2 ))) + ((determinant**2) / (-2.0 * (self.noise_coplanarity**2)) )
w_coplanarity = ((determinant**2) / (-2.0 * (self.noise_coplanarity**2)) )
except:
print("Error on calc coplanarity weight ******")
w_coplanarity = 0.0
# --------------------------- #
# inverse depth * coplanarity #
# --------------------------- #
if(self.count == 0):
#print(w),
#print(w_coplanarity)
pass
w = w + w_coplanarity # use inverse depth * coplanarity log likelihood
#w = w_coplanarity # use only coplanarity log likelihood
#w = w # use only inverse depth log likelihood
weights.append(w)
# ----------------------------------- #
# sum log likelihood of all keypoints #
# ----------------------------------- #
for i,w in enumerate(weights):
if(i==0):
weight = w
else:
weight += w
# ----------------------------- #
# Average of log likelihood sum #
# ----------------------------- #
weight /= float(len(weights))
###############################
#print("weight "+str(weight))
if(self.count == 0):
#print("weight "+str(weight))
#print("first_ratio "+str(float(count_of_first_observation)/float(len(keypoints)))),
#print("("+str(count_of_first_observation)+"/"+str(len(keypoints))+")")
pass
###########################
# 前回ランドマークをすべて消す
for key in prevLandmarkKeys:
del X_.landmarks[key]
self.count+=1
return X_, weight
def predictionAndUpdateOneParticle(self, X, dt, dt2, noise, keypoints,
step, P):
"""
FastSLAM1.0
"""
weight = 0.0 # weight (return value)
weights = []
count_of_known_keypoints = 0
# 姿勢予測 prediction of position
X_ = Particle()
X_.landmarks = X.landmarks
X_.x = X.x + dt * X.v + dt2 * X.a + self.dt2_camera * noise
X_.v = X.v + dt * X.a + self.dt_camera * noise
X_.a = X.a
X_.o = X.o
for keypoint in keypoints:
# previous landmark id
prevLandmarkId = (step - 1) * 10000 + keypoint.prevIndex
# new landmark id
landmarkId = step * 10000 + keypoint.index
# The landmark is already observed or not?
if (X_.landmarks.has_key(prevLandmarkId) == False):
# Fisrt observation
# Initialize landmark and append to particle
landmark = Landmark()
landmark.init(X_, keypoint, P, self.focus)
X_.landmarks[landmarkId] = landmark
else:
# Already observed
count_of_known_keypoints += 1
X_.landmarks[landmarkId] = X_.landmarks[prevLandmarkId]
del X_.landmarks[prevLandmarkId]
# Observation z
z = np.array([keypoint.x, keypoint.y])
# Calc h and H (Jacobian matrix of h)
h, Hx, H = X_.landmarks[landmarkId].calcObservation(
X_, self.focus)
# Kalman filter (Landmark update)
X_.landmarks[landmarkId].mu, X_.landmarks[
landmarkId].sigma, S, Sinv = KF.execEKF1Update(
z, h, X_.landmarks[landmarkId].mu,
X_.landmarks[landmarkId].sigma, H, self.R)
# Calc weight
w = 0.0
try:
w = (1.0 / (math.sqrt(np.linalg.det(2.0 * math.pi * S)))
) * np.exp(-0.5 * ((z - h).T.dot(Sinv.dot(z - h))))
except:
print("Error on calc weight: ")
weights.append(w)
###############################
if (self.count == 0):
#print("z "),
#print(z)
#print("h "),
#print(h)
#print((math.sqrt( np.linalg.det(2.0 * math.pi * S) )))
pass
###############################
for w in weights:
weight += w
"""
if(count_of_known_keypoints > 0):
for i in xrange(len(weights)):
if(i==0):
weight = (100*w)
else:
weight *= (100*w)
"""
###############################
#print("weight "+str(weight))
if (self.count == 0):
#print("weight "+str(weight))
pass
###########################
self.count += 1
###########################
return X_, weight
def predictionAndUpdateOneParticle2(self, X, dt, dt2, keypoints, step, P):
"""
FastSLAM2.0
"""
RSS = [] # residual sum of squares
weight = 0.0 # weight (return value)
weights = []
count_of_known_keypoints = 0
x_diff_sum = np.array([0.0, 0.0, 0.0])
x_diffs = []
# 姿勢予測 prediction of position
X_ = Particle()
X_.landmarks = X.landmarks
X_.x = X.x + dt * X.v + dt2 * X.a
X_.v = X.v + dt * X.a
X_.a = X.a
X_.o = X.o
for keypoint in keypoints:
#############################
start_time_ = time.clock()
#############################
# previous landmark id
prevLandmarkId = (step - 1) * 10000 + keypoint.prevIndex
# new landmark id
landmarkId = step * 10000 + keypoint.index
# The landmark is already observed or not?
if (X_.landmarks.has_key(prevLandmarkId) == False):
# Fisrt observation
# Initialize landmark and append to particle
landmark = Landmark()
landmark.init(X, keypoint, P, self.focus)
X_.landmarks[landmarkId] = landmark
else:
# Already observed
count_of_known_keypoints += 1
X_.landmarks[landmarkId] = X_.landmarks[prevLandmarkId]
del X_.landmarks[prevLandmarkId]
# Actual observation z
z = np.array([keypoint.x, keypoint.y])
# 計測予測 prediction of observation
# Calc h(x), Hx, Hm (Jacobian matrix of h with respect to X and Landmark)
z__, Hx, Hm = X_.landmarks[landmarkId].calcObservation(
X_, self.focus)
# 姿勢のカルマンフィルタ Kalman filter of position
"""
mu_ = copy.deepcopy(X_.landmarks[landmarkId].mu)
sigma_ = copy.deepcopy(X_.landmarks[landmarkId].sigma)
S = Hm.dot(sigma_.dot(Hm.T)) + self.R
L = S + Hx.dot(self.Q.dot(Hx.T))
Sinv = np.linalg.inv(S)
Linv = np.linalg.inv(L)
sigmax = np.linalg.inv( Hx.T.dot(Sinv.dot(Hx)) + np.linalg.inv(self.Q) )
mux = sigmax.dot(Hx.T.dot(Sinv.dot(z - z__)))
# 姿勢のサンプリング sampling of position
x_diff = np.random.multivariate_normal(mux, sigmax)
x_diffs.append(x_diff)
x_diff_sum += x_diff
# 計測再予測 reprediction of observation
z_ = X_.landmarks[landmarkId].h(X_.x + x_diff, X_.o, self.focus)
# ランドマークの予測 prediction of landmark
K = X_.landmarks[landmarkId].sigma.dot(Hm.T.dot(Sinv))
X_.landmarks[landmarkId].mu += K.dot(z - z_)
X_.landmarks[landmarkId].sigma = X_.landmarks[landmarkId].sigma - K.dot(Hm.dot(X_.landmarks[landmarkId].sigma))
"""
S = Hm.dot(X_.landmarks[landmarkId].sigma.dot(Hm.T)) + self.R
L = S + Hx.dot(self.Q.dot(Hx.T))
Sinv = np.linalg.inv(S)
Linv = np.linalg.inv(L)
sigmax = np.linalg.inv(
Hx.T.dot(Sinv.dot(Hx)) + np.linalg.inv(self.Q))
mux = sigmax.dot(Hx.T.dot(Sinv.dot(z - z__)))
# 姿勢のサンプリング sampling of position
x_diff = np.random.multivariate_normal(mux, sigmax)
x_diffs.append(x_diff)
x_diff_sum += x_diff
# 計測再予測 reprediction of observation
z_, XYZ = X_.landmarks[landmarkId].h(X_.x + x_diff, X_.o,
self.focus)
# ランドマークの予測 prediction of landmark
K = X_.landmarks[landmarkId].sigma.dot(Hm.T.dot(Sinv))
X_.landmarks[landmarkId].mu += K.dot(z - z_)
X_.landmarks[
landmarkId].sigma = X_.landmarks[landmarkId].sigma - K.dot(
Hm.dot(X_.landmarks[landmarkId].sigma))
# 重み計算 calc weight
rss_ = (z - z_).T.dot(Linv.dot(z - z_))
RSS.append(rss_)
w = (1.0 /
(math.sqrt(np.linalg.det(2.0 * math.pi * L)))) * np.exp(
-0.5 * (rss_))
weights.append(w)
###############################
end_time_ = time.clock()
if (self.count == 0):
#print(XYZ)
#print(x_diff)
#print ""+str(landmarkId)+" update time = %f" %(end_time_-start_time_)
pass
###############################
if (count_of_known_keypoints > 0):
# 残差が最小のときの重みと位置を採用する
max_index = RSS.index(min(RSS))
weight = weights[max_index]
weight *= 1000
X_.x += x_diffs[max_index]
X_.v += (2.0 * x_diffs[max_index]) / self.dt_camera
###############################
#print("weight "+str(weight))
#if(self.count == 0):
#print("weight "+str(weight))
###########################
self.count += 1
###########################
return X_, weight
def predictionAndUpdateOneParticle(self, X, dt, dt2, noise, keypoints,
step, P, X1, P1, dt_camera, noise_o):
"""
FastSLAM1.0
"""
weight = 0.0 # weight (return value)
weights = []
count_of_first_observation = 0
# 姿勢予測 prediction of position
X_ = Particle()
X_.landmarks = X.landmarks
X_.x = X.x + dt * X.v + dt2 * X.a + noise
#X_.v = X.v + dt*X.a
#X_.v = X.v + dt*X.a + noise*25
#X_.v = X.v + dt*X.a + noise*50
X_.a = X.a
X_.o = X.o
#X_.o = X.o + noise_o
# 速度調整 velocity adjustment
#X_.v = ((X_.x - X1.x)/dt_camera)
X_.v = ((X_.x - X1.x) / dt_camera) * 0.25
#X_.v = ((X_.x - X1.x)/dt_camera)*0.5
# 共面条件モデルのための計算 Calc for Coplanarity
xyz = np.array(
[X_.x[0] - X1.x[0], X_.x[1] - X1.x[1], X_.x[2] - X1.x[2]])
# 前回ランドマークの全てのキーを取得しておく あとで消す
prevLandmarkKeys = []
for key in X_.landmarks:
prevLandmarkKeys.append(key)
for keypoint in keypoints:
# ---------------------------- #
# Calc weight of Inverse Depth #
# ---------------------------- #
# previous landmark id
prevLandmarkId = (step - 1) * 10000 + keypoint.prevIndex
# new landmark id
landmarkId = step * 10000 + keypoint.index
# The landmark is already observed or not?
if (X_.landmarks.has_key(prevLandmarkId) == False):
# Fisrt observation
# Initialize landmark and append to particle
landmark = Landmark()
landmark.initPrev(X1, keypoint, P1, self.focus)
X_.landmarks[landmarkId] = landmark
if (self.count == 0):
count_of_first_observation += 1
else:
# Already observed
X_.landmarks[landmarkId] = X_.landmarks[prevLandmarkId]
#del X_.landmarks[prevLandmarkId]
# Update landmark and calc weight
# Observation z
z = np.array([keypoint.x, keypoint.y])
# Calc h and H (Jacobian matrix of h)
h, Hx, H = X_.landmarks[landmarkId].calcObservation(X_, self.focus)
# Kalman filter (Landmark update)
S = H.dot(X_.landmarks[landmarkId].sigma.dot(H.T)) + self.R
Sinv = np.linalg.inv(S)
K = X_.landmarks[landmarkId].sigma.dot(H.T.dot(Sinv))
X_.landmarks[landmarkId].mu += K.dot(z - h)
X_.landmarks[
landmarkId].sigma = X_.landmarks[landmarkId].sigma - K.dot(
H.dot(X_.landmarks[landmarkId].sigma))
# Calc weight
w = 0.0
try:
#w = (1.0 / (math.sqrt( np.linalg.det(2.0 * math.pi * S) ))) * np.exp( -0.5 * ( (z-h).T.dot(Sinv.dot(z-h)) ) )
#w = (1.0 / (math.sqrt( np.linalg.det(2.0 * math.pi * self.R) ))) * np.exp( -0.5 * ( (z-h).T.dot(self.Rinv.dot(z-h)) ) )
# log likelihood
#w = np.log(1.0 / (math.sqrt( np.linalg.det(2.0 * math.pi * self.R) ))) + ( -0.5 * ( (z-h).T.dot(self.Rinv.dot(z-h)) ) )
w = (-0.5 * ((z - h).T.dot(self.Rinv.dot(z - h))))
except:
print("Error on calc inverse weight ******")
w = 0.0
# -------------------------- #
# Calc weight of Coplanarity #
# -------------------------- #
# Generate uvw1 (time:t-1)
uvf1 = np.array([keypoint.x1, -keypoint.y1, -self.focus
]) # Camera coordinates -> Device coordinates
rotX = Util.rotationMatrixX(X1.o[0])
rotY = Util.rotationMatrixY(X1.o[1])
rotZ = Util.rotationMatrixZ(X1.o[2])
# uvw1 = R(z)R(y)R(x)uvf1
uvw1 = np.dot(rotZ, np.dot(rotY, np.dot(rotX, uvf1)))
uvw1 /= 100.0 # Adjust scale to decrease calculation error. This doesn't have an influence to estimation.
# Generate uvw2 (time:t)
uvf2 = np.array([keypoint.x, -keypoint.y, -self.focus
]) # Camera coordinates -> Device coordinates
rotX = Util.rotationMatrixX(X_.o[0])
rotY = Util.rotationMatrixY(X_.o[1])
rotZ = Util.rotationMatrixZ(X_.o[2])
# uvw2 = R(z)R(y)R(x)uvf2
uvw2 = np.dot(rotZ, np.dot(rotY, np.dot(rotX, uvf2)))
uvw2 /= 100.0 # Adjust scale to decrease calculation error. This doesn't have an influence to estimation.
# Generate coplanarity matrix
coplanarity_matrix = np.array([xyz, uvw1, uvw2])
# Calc determinant
determinant = np.linalg.det(coplanarity_matrix)
# Weight
w_coplanarity = 0.0
try:
#w_coplanarity = (1.0 / (math.sqrt( 2.0 * math.pi * self.noise_coplanarity**2 ))) * np.exp((determinant**2) / (-2.0 * (self.noise_coplanarity**2)) )
# log likelihood
#w_coplanarity = np.log(1.0 / (math.sqrt( 2.0 * math.pi * self.noise_coplanarity**2 ))) + ((determinant**2) / (-2.0 * (self.noise_coplanarity**2)) )
w_coplanarity = ((determinant**2) /
(-2.0 * (self.noise_coplanarity**2)))
except:
print("Error on calc coplanarity weight ******")
w_coplanarity = 0.0
# --------------------------- #
# inverse depth * coplanarity #
# --------------------------- #
if (self.count == 0):
#print(w),
#print(w_coplanarity)
pass
w = w + w_coplanarity # use inverse depth * coplanarity log likelihood
#w = w_coplanarity # use only coplanarity log likelihood
#w = w # use only inverse depth log likelihood
weights.append(w)
# ----------------------------------- #
# sum log likelihood of all keypoints #
# ----------------------------------- #
for i, w in enumerate(weights):
if (i == 0):
weight = w
else:
weight += w
# ----------------------------- #
# Average of log likelihood sum #
# ----------------------------- #
weight /= float(len(weights))
###############################
#print("weight "+str(weight))
if (self.count == 0):
#print("weight "+str(weight))
#print("first_ratio "+str(float(count_of_first_observation)/float(len(keypoints)))),
#print("("+str(count_of_first_observation)+"/"+str(len(keypoints))+")")
pass
###########################
# 前回ランドマークをすべて消す
for key in prevLandmarkKeys:
del X_.landmarks[key]
self.count += 1
return X_, weight
