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STAVPR_nonumba.py
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STAVPR_nonumba.py
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# -*- coding: UTF-8 -*-
#-------------------------------------------
# the code for STA-VPR proposed in "STA-VPR: Spatio-Temporal Alignment for Visual Place Recognition" IEEE RA-L 2021.
# Time: 2021-4-15
# Author: Feng Lu ([email protected])
#-------------------------------------------
import numpy as np
import math
from sklearn import random_projection
from tqdm import tqdm
from scipy.spatial.distance import pdist
def LMDTW(D_CH):
Hl=D_CH.shape[1] # length of seq H
Cl=20 #length of seq C
Tl=40 #length of candidate seq T'
tempList = np.zeros([Hl, 2]) # for save the information (distance with query seq, length) of H_len candidate seqs.
for i in range(Hl):
tempList[i][0]=1.0 #initialize seq distance to a value >=1
for k in range(Hl):
# note that D is distance matrix before dynamic programming, but is cumulative distance matrix after dynamic programming.
D = np.zeros([Cl, Tl]) # record image distance between image i and iamge j, i.e. distance matrix
C = np.zeros([Cl, Tl]) # record total cost C from point (0,0) to (i,j)
# --------------copy distance matrix-------------------------------------
for i in range(Cl):
for j in range(Tl):
if(k+j<Hl):
D[i][j] = D_CH[i][k + j]
else:
D[i][j] = 3 #if out of range, padding with a large number
# -------------------dynamic programming--------------------------------------
# note that after the update, D is cumulative distance matrix(i.e. matrix S in our paper)
C[0][0] = 1
for i in range(1, Cl):
D[i][0] = D[i][0] + D[i - 1][0]
C[i][0] = 1 + C[i - 1][0]
for j in range(1, Tl):
D[0][j] = D[0][j] + D[0][j - 1]
C[0][j] = 1 + C[0][j - 1]
for i in range(1, Cl):
for j in range(1, Tl):
if(D[i - 1][j] <= D[i][j - 1] and D[i - 1][j] <=D[i - 1][j - 1]):
D[i][j] = D[i][j] + D[i - 1][j]
C[i][j] = 1 + C[i - 1][j]
elif (D[i][j - 1] < D[i - 1][j - 1] and D[i][j - 1] < D[i - 1][j]):
D[i][j] = D[i][j] + D[i][j - 1]
C[i][j] = 1 + C[i][j - 1]
else:
D[i][j] = D[i][j] + D[i - 1][j - 1]
C[i][j] = 1 + C[i - 1][j - 1]
#-----------find the best local sebsequence of k-th candidate seq T for matching query seq---------------
for j in range(Tl):
if(D[Cl - 1][j]/C[Cl - 1][j]<tempList[k][0]):
tempList[k][0]=D[Cl - 1][j]/C[Cl - 1][j] #for save seq distance between k-th candidate seq T and query seq
tempList[k][1]=j #for save length of k-th candidate seq T
#----------find the start ID of the matched result seq T (that have min seq distance)--------------
minDistanIndex = 0 #initialize start ID
for i in range(1,Hl):
if(tempList[i][0]<tempList[minDistanIndex][0]):
minDistanIndex=i
# get all information of matched result seq T
minSeqDis=tempList[minDistanIndex][0] #min seq distance (between seq C and T)
startID=minDistanIndex #start ID of matched result seq T
endID=minDistanIndex+tempList[minDistanIndex][1] #end ID of matched result seq T
return minSeqDis, startID, endID
def alignDistance(aC,H,flag=True):
l_aC = len(aC)
l_H = len(H)
D_aCH = np.zeros([l_aC, l_H])
sta = "start to compute distance matrix D_aCH"
for ii in tqdm(range(D_aCH.shape[0]), sta):
for jj in range(D_aCH.shape[1]):
l1 = 7
l2 = 7
#note that D is distance matrix before dynamic programming, but is cumulative distance matrix after dynamic programming.
D = np.zeros([l1, l2]) # record image distance between image i and iamge j, i.e. distance matrix
C = np.zeros([l1, l2]) # record total cost C from point (0,0) to (i,j)
# --------------compute distance matrix-------------------------------------
for i in range(l1):
for j in range(l2):
if flag and (i-j>2 or i-j<-2):
D[i][j] = 3.0 #when apply restricted alignment, set some points to a large number directly
else:
D[i][j] = pdist([aC[ii][i],H[jj][j]], 'cosine')
# --------------compute adaptive parameter a---------------------------------
I3 = np.argmin(D[3, :]) #the argmin of the third row
a = math.sqrt(1.0 + abs(I3 - 3))
#-------------------dynamic programming--------------------------------------
#note that after the update, D is cumulative distance matrix(i.e. matrix S in our paper)
C[0][0] = 1
for i in range(1, l1):
D[i][0] = D[i][0] + D[i - 1][0]
C[i][0] = 1 + C[i - 1][0]
for j in range(1, l2):
D[0][j] = D[0][j] + D[0][j - 1]
C[0][j] = 1 + C[0][j - 1]
for i in range(1, l1):
for j in range(1, l2):
cand1 = D[i][j] + D[i - 1][j]
cand2 = D[i][j] + D[i][j - 1]
cand3 = a * D[i][j] + D[i - 1][j - 1]
tempMin=min(cand1,cand2,cand3)
if(tempMin==cand1):
D[i][j] = cand1
C[i][j] = 1 + C[i - 1][j]
elif(tempMin==cand2):
D[i][j] = cand2
C[i][j] = 1 + C[i][j - 1]
elif(tempMin==cand3):
D[i][j] = cand3
C[i][j] = a + C[i - 1][j - 1]
D_aCH[ii][jj]=D[-1][-1]/C[-1][-1]
return D_aCH
def cosineDistance(aC,H):
l_aC = len(aC)
l_H = len(H)
D_aCH = np.zeros([l_aC, l_H])
sta = "start to compute distance matrix D_aCH"
for i in tqdm(range(D_aCH.shape[0]), sta):
for j in range(D_aCH.shape[1]):
D_aCH[i][j]=pdist([aC[i],H[j]], 'cosine')
return D_aCH
def RandomProject(H,aC):
print("start to reduce dimension!")
HS, aCS = np.shape(H), np.shape(aC)
rp_H, rp_aC = np.array(H), np.array(aC)
rp_H, rp_aC = rp_H.reshape(HS[0] * HS[1], HS[2]), rp_aC.reshape(aCS[0] * aCS[1], aCS[2])
reducedD = 512
transformer = random_projection.GaussianRandomProjection(n_components=reducedD).fit(rp_H)
H, aC = [], []
for i in range(HS[0]):
H.append(transformer.transform(rp_H[7 * i: 7 * i + 7]))
for i in range(aCS[0]):
aC.append(transformer.transform(rp_aC[7 * i: 7 * i + 7]))
# rp_H = transformer.transform(rp_H)
# rp_aC = transformer.transform(rp_aC)
# H = rp_H.reshape(HS[0], HS[1], reducedD)
# aC = rp_aC.reshape(aCS[0], aCS[1], reducedD)
# H = H.tolist()
# aC = aC.tolist()
print("datasize before GRP (reference):",HS)
print("datasize after GRP (reference):", np.shape(H))
return H,aC