def mergeModel(sfm_data_dirA,
sfm_data_dirB,
locFolderB,
outfile,
ransacK=1.0,
ransacRound=10000,
inputImgDir="",
minLimit=4,
svdRatio=1.75):
print "Loading sfm_data"
sfm_dataB = FileUtils.loadjson(sfm_data_dirB)
# read matching pairs from localization result
imgnameB, matchlistB = readMatch(locFolderB)
# get viewID from image name for model B
viewIDB = imgnameToViewID(imgnameB, sfm_dataB)
# get mapping between viewID,featID to 3D point ID
viewFeatMapB = getViewFeatTo3DMap(sfm_dataB)
# find consistent match between 3D of model B to 3D of model A
print "Calculating consistent 3D matches"
match3D_BA = getConsistent3DMatch(viewIDB, matchlistB, viewFeatMapB)
print "Found " + str(len(match3D_BA)) + " consistent matches"
# not enough matches
if len(match3D_BA) <= 4 or len(match3D_BA) <= minLimit:
return len(match3D_BA), [0]
# move the load of larger model here to reduce time if merging is not possible
sfm_dataA = FileUtils.loadjson(sfm_data_dirA)
# get 3D point. Note that element 0 of each pair in match3D_BA
# is 3D pt ID of model B and element 1 is that of model A
print "Load 3D points"
pointA = get3DPointloc(sfm_dataA, [x[1] for x in match3D_BA])
pointB = get3DPointloc(sfm_dataB, [x[0] for x in match3D_BA])
pointAn = np.asarray(pointA, dtype=np.float).T
pointBn = np.asarray(pointB, dtype=np.float).T
# calculate ransac threshold
# calculate as 4 times the median of distance between
# 3D pt of A
print "Find transformation with RANSAC"
# modified by T.Ishihara 2016.04.08
# median of camera positions merge too many points, use median of structure points instead
#ransacThres = findMedianThres(sfm_dataA, ransacK)
ransacThres = findMedianStructurePointsThres(sfm_dataA, ransacK)
# TODO : replace with RANSAC similarity transform
# find robust transformation
M, inliers = ransacAffineTransform(pointAn, pointBn, ransacThres,
ransacRound, svdRatio)
# cannot find RANSAC transformation
if (len(inliers) == 0):
return len(match3D_BA), [0]
print M
# stop if not enough inliers
sSvd = np.linalg.svd(M[0:3, 0:3], compute_uv=0)
if len(inliers) <= 4 or sSvd[0] / sSvd[-1] > svdRatio:
return len(inliers), M
# perform merge
# last argument is map from inliers 3D pt Id of model B to that of model A
print "Merging sfm_data"
merge_sfm_data(sfm_dataA, sfm_dataB, M,
{match3D_BA[x][0]: match3D_BA[x][1]
for x in inliers})
# change input image folder
if inputImgDir != "":
sfm_dataA["root_path"] = inputImgDir
# save json file
print "Saving json file"
FileUtils.savejson(sfm_dataA, outfile)
# return number of inliers for transformation
return len(inliers), M
def cleanSfM(sfm_data_path,matchesFile):
sfm_data = FileUtils.loadjson(sfm_data_path)
if (len(sfm_data["views"])==0):
print "No views are used in reconstruction of " + sfm_data_path
return [[],[]]
if (len(sfm_data["extrinsics"])==0):
print "No extrinsics are used in reconstruction of " + sfm_data_path
return [[],[]]
# get map from ID to index
viewMap = {}
for i in range(0,len(sfm_data["views"])):
viewMap[sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["id_view"]] = i
extMap = {}
for i in range(0,len(sfm_data["extrinsics"])):
extMap[sfm_data["extrinsics"][i]["key"]] = i
strMap = {}
for i in range(0,len(sfm_data["structure"])):
strMap[sfm_data["structure"][i]["key"]] = i
# find viewIDs of first and last frame used in reconstruction
firstViewID = len(sfm_data["views"])
lastViewID = 0
firstExtID = min(extMap.keys())
for i in range(0,len(sfm_data["views"])):
viewID = sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["id_view"]
extID = sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["id_pose"]
if extID in extMap:
if firstViewID > viewID:
firstViewID = viewID
if lastViewID < viewID:
lastViewID = viewID
if firstViewID >= lastViewID:
print "No views are used in reconstruction of " + sfm_data_path
return [[],[]]
# get list of unused view back to front
# and change the view Index
unusedImgName = [[],[]]
for i in range(len(sfm_data["views"])-1,lastViewID,-1):
unusedImgName[1].append(sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["filename"])
sfm_data["views"].pop(i)
for i in range(lastViewID,firstViewID-1,-1):
newViewID = sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["id_view"]-firstViewID
sfm_data["views"][i]["key"] = newViewID
sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["id_view"] = newViewID
sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["id_pose"] = sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["id_pose"] - firstExtID
for i in range(firstViewID-1,-1,-1):
unusedImgName[0].append(sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["filename"])
sfm_data["views"].pop(i)
# change extrinsics ID
for i in range(0,len(sfm_data["extrinsics"])):
sfm_data["extrinsics"][i]["key"] = sfm_data["extrinsics"][i]["key"]-firstExtID
# change index of refered view in structure
for i in range(0,len(sfm_data["structure"])):
for j in range(0,len(sfm_data["structure"][i]["value"]["observations"])):
sfm_data["structure"][i]["value"]["observations"][j]["key"] = \
sfm_data["structure"][i]["value"]["observations"][j]["key"]-firstViewID
# save jsonfile back
FileUtils.savejson(sfm_data,sfm_data_path)
# update matches file
for matchfile in matchesFile:
matchFileName = os.path.basename(matchfile)
matchFileNameTmp = matchFileName.join(random.choice(string.lowercase) for i in range(10)) #random name
matchDir = os.path.dirname(matchfile)
fout = open(os.path.join(matchDir,matchFileNameTmp),"w")
with open(matchfile,"r") as mfile:
mode = 0
write = False
countLine = 0
for line in mfile:
line = line.strip()
if mode == 0:
line = line.split(" ")
view1 = int(line[0])
view2 = int(line[1])
if view1 < firstViewID or view1 > lastViewID or \
view2 < firstViewID or view2 > lastViewID:
write = False
else:
write = True
if write:
# update viewID and write out
fout.write(str(int(line[0])-firstViewID))
fout.write(" ")
fout.write(str(int(line[1])-firstViewID))
fout.write("\n")
countLine = 0
mode = 1
elif mode == 1:
numMatch= int(line)
if write:
# get number of matches and write out
fout.write(line + "\n")
mode = 2
elif mode == 2:
if write:
# write out matches
fout.write(line + "\n")
countLine = countLine + 1
if countLine == numMatch:
mode = 0
os.rename(os.path.join(matchDir,matchFileName),os.path.join(matchDir,matchFileName+"_old"))
os.rename(os.path.join(matchDir,matchFileNameTmp),os.path.join(matchDir,matchFileName))
return unusedImgName
def mergeModel(sfm_data_dirA, sfm_data_dirB, locFolderB, outfile, ransacThres, mergePointThres, ransacRoundMul=100, inputImgDir="", minLimit=4, svdRatio=1.75):
print "Loading sfm_data"
sfm_dataB = FileUtils.loadjson(sfm_data_dirB)
# read matching pairs from localization result
imgnameB, matchlistB = readMatch(locFolderB)
# get viewID from image name for model B
viewIDB = imgnameToViewID(imgnameB, sfm_dataB)
# get mapping between viewID,featID to 3D point ID
viewFeatMapB = getViewFeatTo3DMap(sfm_dataB)
# find consistent match between 3D of model B to 3D of model A
print "Calculating consistent 3D matches"
match3D_BA = getConsistent3DMatch(viewIDB, matchlistB, viewFeatMapB)
print "Found " + str(len(match3D_BA)) + " consistent matches"
# not enough matches
if len(match3D_BA) <= 4 or len(match3D_BA) <= minLimit:
return len(match3D_BA), len(match3D_BA), np.asarray([])
# move the load of larger model here to reduce time if merging is not possible
sfm_dataA = FileUtils.loadjson(sfm_data_dirA)
# get 3D point. Note that element 0 of each pair in match3D_BA
# is 3D pt ID of model B and element 1 is that of model A
print "Load 3D points"
pointA = get3DPointloc(sfm_dataA, [x[1] for x in match3D_BA])
pointB = get3DPointloc(sfm_dataB, [x[0] for x in match3D_BA])
pointAn = np.asarray(pointA, dtype=np.float).T
pointBn = np.asarray(pointB, dtype=np.float).T
# find robust transformation
print "Find transformation with RANSAC"
ransacRound = len(match3D_BA)*ransacRoundMul
print "Number of RANSAC round : " + str(ransacRound)
M, inliers = ransacTransform(pointAn, pointBn, ransacThres, ransacRound, svdRatio)
# cannot find RANSAC transformation
if (M.size==0):
return len(match3D_BA), len(match3D_BA), np.asarray([])
print M
# stop if not enough inliers
sSvd = np.linalg.svd(M[0:3,0:3],compute_uv=0)
# fixed by T.Ishihara to use minLimit 2016.06.06
#if len(inliers) <= 4 or sSvd[0]/sSvd[-1] > svdRatio:
if len(inliers) <= minLimit or sSvd[0]/sSvd[-1] > svdRatio:
return len(match3D_BA), len(inliers), M
# perform merge
# last argument is map from inliers 3D pt Id of model B to that of model A
print "Merging sfm_data"
# fixed by T. Ishihara, use different parameter to find ransac inlier and merge points inliers
'''
merge_sfm_data(sfm_dataA, sfm_dataB, M, {match3D_BA[x][0]: match3D_BA[x][1] for x in inliers})
'''
mergePointInliers = getInliersByAffineTransform(pointAn, pointBn, M, mergePointThres)
merge_sfm_data(sfm_dataA, sfm_dataB, M, {match3D_BA[x][0]: match3D_BA[x][1] for x in mergePointInliers})
# change input image folder
if inputImgDir != "":
sfm_dataA["root_path"] = inputImgDir
# save json file
print "Saving json file"
FileUtils.savejson(sfm_dataA,outfile)
# return number of inliers for transformation
return len(match3D_BA), len(inliers), M
def cleanSfM(sfm_data_path, matchesFile):
sfm_data = FileUtils.loadjson(sfm_data_path)
if (len(sfm_data["views"]) == 0):
print "No views are used in reconstruction of " + sfm_data_path
return [[], []]
if (len(sfm_data["extrinsics"]) == 0):
print "No extrinsics are used in reconstruction of " + sfm_data_path
return [[], []]
# get map from ID to index
viewMap = {}
for i in range(0, len(sfm_data["views"])):
viewMap[sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]
["id_view"]] = i
extMap = {}
for i in range(0, len(sfm_data["extrinsics"])):
extMap[sfm_data["extrinsics"][i]["key"]] = i
strMap = {}
for i in range(0, len(sfm_data["structure"])):
strMap[sfm_data["structure"][i]["key"]] = i
# find viewIDs of first and last frame used in reconstruction
firstViewID = len(sfm_data["views"])
lastViewID = 0
firstExtID = min(extMap.keys())
for i in range(0, len(sfm_data["views"])):
viewID = sfm_data["views"][i]["value"]["ptr_wrapper"]["data"][
"id_view"]
extID = sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["id_pose"]
if extID in extMap:
if firstViewID > viewID:
firstViewID = viewID
if lastViewID < viewID:
lastViewID = viewID
if firstViewID >= lastViewID:
print "No views are used in reconstruction of " + sfm_data_path
return [[], []]
# get list of unused view back to front
# and change the view Index
unusedImgName = [[], []]
for i in range(len(sfm_data["views"]) - 1, lastViewID, -1):
unusedImgName[1].append(
sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["filename"])
sfm_data["views"].pop(i)
for i in range(lastViewID, firstViewID - 1, -1):
newViewID = sfm_data["views"][i]["value"]["ptr_wrapper"]["data"][
"id_view"] - firstViewID
sfm_data["views"][i]["key"] = newViewID
sfm_data["views"][i]["value"]["ptr_wrapper"]["data"][
"id_view"] = newViewID
sfm_data["views"][i]["value"]["ptr_wrapper"]["data"][
"id_pose"] = sfm_data["views"][i]["value"]["ptr_wrapper"]["data"][
"id_pose"] - firstExtID
for i in range(firstViewID - 1, -1, -1):
unusedImgName[0].append(
sfm_data["views"][i]["value"]["ptr_wrapper"]["data"]["filename"])
sfm_data["views"].pop(i)
# change extrinsics ID
for i in range(0, len(sfm_data["extrinsics"])):
sfm_data["extrinsics"][i][
"key"] = sfm_data["extrinsics"][i]["key"] - firstExtID
# change index of refered view in structure
for i in range(0, len(sfm_data["structure"])):
for j in range(0,
len(sfm_data["structure"][i]["value"]["observations"])):
sfm_data["structure"][i]["value"]["observations"][j]["key"] = \
sfm_data["structure"][i]["value"]["observations"][j]["key"]-firstViewID
# save jsonfile back
FileUtils.savejson(sfm_data, sfm_data_path)
# update matches file
for matchfile in matchesFile:
matchFileName = os.path.basename(matchfile)
matchFileNameTmp = matchFileName.join(
random.choice(string.lowercase) for i in range(10)) #random name
matchDir = os.path.dirname(matchfile)
fout = open(os.path.join(matchDir, matchFileNameTmp), "w")
with open(matchfile, "r") as mfile:
mode = 0
write = False
countLine = 0
for line in mfile:
line = line.strip()
if mode == 0:
line = line.split(" ")
view1 = int(line[0])
view2 = int(line[1])
if view1 < firstViewID or view1 > lastViewID or \
view2 < firstViewID or view2 > lastViewID:
write = False
else:
write = True
if write:
# update viewID and write out
fout.write(str(int(line[0]) - firstViewID))
fout.write(" ")
fout.write(str(int(line[1]) - firstViewID))
fout.write("\n")
countLine = 0
mode = 1
elif mode == 1:
numMatch = int(line)
if write:
# get number of matches and write out
fout.write(line + "\n")
mode = 2
elif mode == 2:
if write:
# write out matches
fout.write(line + "\n")
countLine = countLine + 1
if countLine == numMatch:
mode = 0
os.rename(os.path.join(matchDir, matchFileName),
os.path.join(matchDir, matchFileName + "_old"))
os.rename(os.path.join(matchDir, matchFileNameTmp),
os.path.join(matchDir, matchFileName))
return unusedImgName
def mergeModel(sfm_data_dirA, sfm_data_dirB, locFolderB, outfile, ransacThres, mergePointThres, ransacRoundMul=100, inputImgDir="", minLimit=4, svdRatio=1.75):
print "Loading sfm_data"
sfm_dataB = FileUtils.loadjson(sfm_data_dirB)
# read matching pairs from localization result
imgnameB, matchlistB = readMatch(locFolderB)
# get viewID from image name for model B
viewIDB = imgnameToViewID(imgnameB, sfm_dataB)
# get mapping between viewID,featID to 3D point ID
viewFeatMapB = getViewFeatTo3DMap(sfm_dataB)
# find consistent match between 3D of model B to 3D of model A
print "Calculating consistent 3D matches"
match3D_BA = getConsistent3DMatch(viewIDB, matchlistB, viewFeatMapB)
print "Found " + str(len(match3D_BA)) + " consistent matches"
# not enough matches
if len(match3D_BA) <= 4 or len(match3D_BA) <= minLimit:
return len(match3D_BA), len(match3D_BA), np.asarray([])
# move the load of larger model here to reduce time if merging is not possible
sfm_dataA = FileUtils.loadjson(sfm_data_dirA)
# get 3D point. Note that element 0 of each pair in match3D_BA
# is 3D pt ID of model B and element 1 is that of model A
print "Load 3D points"
pointA = get3DPointloc(sfm_dataA, [x[1] for x in match3D_BA])
pointB = get3DPointloc(sfm_dataB, [x[0] for x in match3D_BA])
pointAn = np.asarray(pointA, dtype=np.float).T
pointBn = np.asarray(pointB, dtype=np.float).T
# find robust transformation
print "Find transformation with RANSAC"
ransacRound = len(match3D_BA)*ransacRoundMul
print "Number of RANSAC round : " + str(ransacRound)
M, inliers = ransacTransform(pointAn, pointBn, ransacThres, ransacRound, svdRatio)
# cannot find RANSAC transformation
if (M.size==0):
return len(match3D_BA), len(match3D_BA), np.asarray([])
print M
# stop if not enough inliers
sSvd = np.linalg.svd(M[0:3,0:3],compute_uv=0)
# fixed by T.Ishihara to use minLimit 2016.06.06
#if len(inliers) <= 4 or sSvd[0]/sSvd[-1] > svdRatio:
if len(inliers) <= minLimit or sSvd[0]/sSvd[-1] > svdRatio:
return len(match3D_BA), len(inliers), M
# perform merge
# last argument is map from inliers 3D pt Id of model B to that of model A
print "Merging sfm_data"
# fixed by T. Ishihara, use different parameter to find ransac inlier and merge points inliers
'''
merge_sfm_data(sfm_dataA, sfm_dataB, M, {match3D_BA[x][0]: match3D_BA[x][1] for x in inliers})
'''
mergePointInliers = getInliersByAffineTransform(pointAn, pointBn, M, mergePointThres)
merge_sfm_data(sfm_dataA, sfm_dataB, M, {match3D_BA[x][0]: match3D_BA[x][1] for x in mergePointInliers})
# change input image folder
if inputImgDir != "":
sfm_dataA["root_path"] = inputImgDir
# save json file
print "Saving json file"
FileUtils.savejson(sfm_dataA,outfile)
# return number of inliers for transformation
return len(match3D_BA), len(inliers), M
