def SDPTDoALocate(self, RN1, RN2, TDoA, TDoAStd):
"""
Apply SDP approximation and localization
"""
RN1 = cvxm.matrix(RN1)
RN2 = cvxm.matrix(RN2)
TDoA = cvxm.matrix(TDoA)
c = 3e08
RDoA = c*TDoA
RDoAStd=cvxm.matrix(c*TDoAStd)
mtdoa,ntdoa=cvxm.size(RN1)
Im = cvxm.eye(mtdoa)
Y=cvxm.optvar('Y',mtdoa+1,mtdoa+1)
t=cvxm.optvar('t',ntdoa,1)
prob=cvxm.problem(cvxm.minimize(cvxm.norm2(t)))
prob.constr.append(Y>=0)
prob.constr.append(Y[mtdoa,mtdoa]==1)
for i in range(ntdoa):
X0=cvxm.matrix([[Im, -cvxm.transpose(RN1[:,i])],[-RN1[:,i], cvxm.transpose(RN1[:,i])*RN1[:,i]]])
X1=cvxm.matrix([[Im, -cvxm.transpose(RN2[:,i])],[-RN2[:,i], cvxm.transpose(RN2[:,i])*RN2[:,i]]])
prob.constr.append(-RDoAStd[i,0]*t[i]<cvxm.trace(X0*Y)+cvxm.trace(X1*Y)-RDoA[i,0]**2)
prob.constr.append(RDoAStd[i,0]*t[i]>cvxm.trace(X0*Y)+cvxm.trace(X1*Y)-RDoA[i,0]**2)
prob.solve()
Pval=Y.value
X_cvx=Pval[:2,-1]
return X_cvx
def SDPToALocate(self, RN, ToA, ToAStd):
"""
Apply SDP approximation and localization
"""
RN = cvxm.matrix(RN)
ToA = cvxm.matrix(ToA)
c = 3e08 # Speed of light
RoA = c*ToA
RoAStd = c*ToAStd
RoAStd = cvxm.matrix(RoAStd)
mtoa,ntoa=cvxm.size(RN)
Im = cvxm.eye(mtoa)
Y=cvxm.optvar('Y',mtoa+1,mtoa+1)
t=cvxm.optvar('t',ntoa,1)
prob=cvxm.problem(cvxm.minimize(cvxm.norm2(t)))
prob.constr.append(Y>=0)
prob.constr.append(Y[mtoa,mtoa]==1)
for i in range(ntoa):
X0=cvxm.matrix([[Im, -cvxm.transpose(RN[:,i])],[-RN[:,i], cvxm.transpose(RN[:,i])*RN[:,i]]])
prob.constr.append(-t[i]<(cvxm.trace(X0*Y)-RoA[i]**2)*(1/RoAStd[i]))
prob.constr.append(t[i]>(cvxm.trace(X0*Y)-RoA[i]**2)*(1/RoAStd[i]))
prob.solve()
Pval=Y.value
X_cvx=Pval[:2,-1]
return X_cvx
def SDPRSSLocate(self, RN, PL0, d0, RSS, RSSnp, RSSStd, Rest):
RoA=self.getRange(RN, PL0, d0, RSS, RSSnp, RSSStd, Rest)
RN=cvxm.matrix(RN)
RSS=cvxm.matrix(RSS)
RSSnp=cvxm.matrix(RSSnp)
RSSStd=cvxm.matrix(RSSStd)
PL0=cvxm.matrix(PL0)
RoA=cvxm.matrix(RoA)
mrss,nrss=cvxm.size(RN)
Si = array([(1/d0**2)*10**((RSS[0,0]-PL0[0,0])/(5.0*RSSnp[0,0])),(1/d0**2)*10**((RSS[1,0]-PL0[1,0])/(5.0*RSSnp[1,0])),(1/d0**2)*10**((RSS[2,0]-PL0[2,0])/(5.0*RSSnp[2,0])),(1/d0**2)*10**((RSS[3,0]-PL0[3,0])/(5.0*RSSnp[3,0]))])
#Si = array([(1/d0**2)*10**(-(RSS[0,0]-PL0[0,0])/(5.0*RSSnp[0,0])),(1/d0**2)*10**(-(RSS[0,1]-PL0[1,0])/(5.0*RSSnp[0,1])),(1/d0**2)*10**(-(RSS[0,2]-PL0[2,0])/(5.0*RSSnp[0,2])),(1/d0**2)*10**(-(RSS[0,3]-PL0[3,0])/(5.0*RSSnp[0,3]))])
Im = cvxm.eye(mrss)
Y=cvxm.optvar('Y',mrss+1,mrss+1)
t=cvxm.optvar('t',nrss,1)
prob=cvxm.problem(cvxm.minimize(cvxm.norm2(t)))
prob.constr.append(Y>=0)
prob.constr.append(Y[mrss,mrss]==1)
for i in range(nrss):
X0=cvxm.matrix([[Im, -cvxm.transpose(RN[:,i])],[-RN[:,i], cvxm.transpose(RN[:,i])*RN[:,i]]])
prob.constr.append(-RSSStd[i,0]*t[i]<Si[i]*cvxm.trace(X0*Y)-1)
prob.constr.append(RSSStd[i,0]*t[i]>Si[i]*cvxm.trace(X0*Y)-1)
prob.solve()
Pval=Y.value
X_cvx=Pval[:2,-1]
return X_cvx
def compute_bbox_set_agreement(example_boxes, gold_boxes):
nExB = len(example_boxes)
nGtB = len(gold_boxes)
if nExB == 0:
if nGtB == 0:
return 1
else:
return 0
if nGtB == 0:
print "WARNING: new object"
return 0
A = cvxmod.zeros(rows=nExB, cols=nGtB)
for iBox, ex in enumerate(example_boxes):
for jBox, gt in enumerate(gold_boxes):
A[iBox, jBox] = ex.overlap_score(gt)
S = []
S2 = []
for iBox, ex in enumerate(example_boxes):
S_tmp = [0] * (iBox) * nGtB + [1] * nGtB + [0] * (nExB - iBox -
1) * nGtB
S.append(S_tmp)
for jBox in range(0, nGtB):
S2_tmp = [0] * nExB * nGtB
for j2 in range(0, nExB):
S2_tmp[j2 * nGtB + jBox] = 1
S2.append(S2_tmp)
S = cvxmod.transpose(cvxmod.matrix(S, size=(nExB * nGtB, nExB)))
S2 = cvxmod.transpose(cvxmod.matrix(S2, size=(nExB * nGtB, nGtB)))
A2 = cvxmod.matrix(A, (1, nExB * nGtB))
x = cvxmod.optvar('x', rows=nExB * nGtB, cols=1)
p = cvxmod.problem(cvxmod.maximize(A2 * x))
p.constr.append(x <= 1)
p.constr.append(x >= 0)
p.constr.append(S * x <= 1)
p.constr.append(S2 * x <= 1)
p.solve(True)
overlap = cvxmod.value(p) / max(nExB, nGtB)
assert (overlap < 1.0001)
return overlap