def mergeModel(self, listbeacon, image_descFile, inputPath, outputPath, reconParam=ReconstructParam,
reconIBeaconParam=ReconstructIBeaconParam, reconBOWParam=ReconstructBOWParam,
mergeCoocThresRat=0.25, mergeCoocThresFrame=30):
print "Begin merging models"
normBeaconApproach = reconIBeaconParam.normApproach
FileUtils.makedir(self.mInputImgPath)
FileUtils.makedir(self.mCsvPath)
FileUtils.makedir(self.mMatchesPath)
FileUtils.makedir(self.mSfMPath)
# generate beacon.txt file for all models
# we need generate every time to synchronize with sfm_data.json
for video in self.sfmModel:
print "Generating beacon.txt for " + video.name
# write beacon file
IBeaconUtils.exportBeaconDataForSfmImageFrames(video.csvFolLoc, video.sfm_dataLoc,
listbeacon, video.beaconFileLoc, normBeaconApproach)
if (not os.path.isfile(video.beaconFileLoc)):
print("Error: beacon file %s is not created" % video.beaconFileLoc)
return
# create symbolic links to all images, csv, and descriptor/feature files
os.system("cp --remove-destination -s " + os.path.join(inputPath,"*","inputImg","*") + " " + self.mInputImgPath)
os.system("cp --remove-destination -s " + os.path.join(inputPath,"*","csv","*") + " " + self.mCsvPath)
os.system("cp --remove-destination -s " + os.path.join(outputPath,"*","matches","*.desc") + " " + self.mMatchesPath)
os.system("cp --remove-destination -s " + os.path.join(outputPath,"*","matches","*.feat") + " " + self.mMatchesPath)
os.system("cp --remove-destination -s " + os.path.join(outputPath,"*","matches","*.bow") + " " + self.mMatchesPath)
# copy listbeacon.txt and image_describer.txt
os.system("cp --remove-destination " + listbeacon + " " + self.mInputPath)
os.system("cp --remove-destination " + image_descFile + " " + self.mMatchesPath)
listLead = range(0,len(self.sfmModel)) # list of model indexes which can initiate merge (list of model indexes which did not fail merge yet)
listBye = [] # list of model indexes which will not be used to initiate merge (list of model indexes which already failed merge)
baseVideo = -1
mergeCandidatesRemainsForBaseVideo = True
calBeaconSim = False
while True:
# update model indexes which are not used to initiate merge
if not mergeCandidatesRemainsForBaseVideo:
listBye.append(self.sfmModel[baseVideo].name)
listName = [(x,self.sfmModel[x].name) for x in range(0,len(self.sfmModel))]
listLead = [x[0] for x in listName if x[1] not in listBye]
# if there was a merge, recalculate the cooccurence graph
if mergeCandidatesRemainsForBaseVideo:
# calculate cooccurence graph
if not calBeaconSim:
beaconCoocRat, beaconCoocFrame = self.calCooccurenceGraph(coocThres=reconIBeaconParam.coocThres)
calBeaconSim = True
print "graph edges : " + str(beaconCoocRat)
print "SfM model names : " + str([x.name for x in self.sfmModel])
connectionGraph = np.logical_or(beaconCoocRat > mergeCoocThresRat,beaconCoocFrame > mergeCoocThresFrame)
# calculate connected component on graph
ccLabel = scipy.sparse.csgraph.connected_components(
connectionGraph,
directed=False)[1]
# if nore more mergable components
if len(np.unique(ccLabel)) == len(ccLabel):
print "No more mergable components. Exiting."
return
# sort the length of reconstructed frames in each video
# from small to large to find the base Video
reconFrameLenList = [len(self.sfmModel[i].reconFrame) for i in range(0,len(self.sfmModel))]
reconFrameLenIdx = [x[0] for x in sorted(enumerate(reconFrameLenList), key=lambda y:y[1])]
# find first base video that has a connected component
baseVideo = ""
for video in reconFrameLenIdx:
if np.sum(ccLabel==ccLabel[video]) > 1 and video in listLead:
baseVideo = video
break
# this should never be called since program should exit
# if there is no connected components in grap
if baseVideo == "":
print "Cannot find connected component to merge. Exiting."
return
# get videos that connect to this baseVideo
# and sort the from smallest to largest as merge order
neighborVec = np.where(connectionGraph[baseVideo,:])[0]
neighborVec = neighborVec[neighborVec!=baseVideo] # prevent selecting itself to merge
mergeCandidate = neighborVec.tolist()
nReconFrameMergeCand = [len(self.sfmModel[x].reconFrame) for x in mergeCandidate]
orderMergeCand = [x[0] for x in sorted(enumerate(nReconFrameMergeCand), key=lambda y:y[1])]
mergeCandidateModel = [self.sfmModel[mergeCandidate[i]] for i in orderMergeCand]
mergedModel = self.sfmModel[baseVideo]
print "Based model: " + mergedModel.name
print "To merge with: " + str([x.name for x in mergeCandidateModel])
mergeCandidatesRemainsForBaseVideo = False
for video in mergeCandidateModel:
# check if failed localization has been performed on this pair before
# if so, skip this localization
if self.isBadMatch(video,mergedModel):
continue
# swap order so small model is merged to larger model
swap = False
if len(mergedModel.reconFrame) < len(video.reconFrame):
tmp = mergedModel
mergedModel = video
video = tmp
swap = True
# attempt merge
mergeResult, mergedModelTmp = self.mergeOneModel(mergedModel,video,reconParam,reconIBeaconParam,reconBOWParam)
if mergeResult:
mergedModel.update(mergedModelTmp)
videoIdx = self.sfmModel.index(video)
del self.sfmModel[videoIdx]
# update beacon
beaconCoocRat = np.delete(beaconCoocRat,videoIdx,0)
beaconCoocRat = np.delete(beaconCoocRat,videoIdx,1)
beaconCoocFrame = np.delete(beaconCoocFrame,videoIdx,0)
beaconCoocFrame = np.delete(beaconCoocFrame,videoIdx,1)
beaconCoocRat, beaconCoocFrame = self.updateCooccurenceGraph(beaconCoocRat, beaconCoocFrame, [self.sfmModel.index(mergedModel)], coocThres=reconIBeaconParam.coocThres)
self.nMergedModel = self.nMergedModel+1
self.save(os.path.join(self.mSfMPath,"global" + str(self.nMergedModel-1),"mergeGraph.txt"))
self.save(os.path.join(self.mSfMPath,"mergeGraph.txt"))
mergeCandidatesRemainsForBaseVideo = True
# reset listBye to allow small model to merge to new large model
listBye = []
# write result log file
with open(os.path.join(self.mSfMPath,"logRecon.txt"),"a") as outLogFile:
outLogFile.write(str(self.nMergedModel-1) + " " + mergedModel.name + "\n")
# start again
break
else:
# add to bad matches
self.badMatches.append([video.name,mergedModel.name])
# save
self.save(os.path.join(self.mSfMPath,"mergeGraph.txt"))
if swap:
# swap back if not merged
mergedModel = video
def mergeOneModel(self, model1, model2, reconParam, reconIBeaconParam, reconBOWParam):
sfmOutPath = os.path.join(self.mSfMPath,"global"+str(self.nMergedModel))
# modified by T. IShihara 2016.06.14
# fix file name too long issue
#
# create a temporary folder for reconstructed image of model2
#inputImgTmpFolder = os.path.join(self.mSfMPath,"inputImgTmp","inputImgTmp"+model2.name)
inputImgTmpFolder = os.path.join(self.mSfMPath,"inputImgTmp","inputImgTmpModel2")
if os.path.isdir(inputImgTmpFolder):
FileUtils.removedir(inputImgTmpFolder)
# copy reconstructed image fom model2 to tmp folder
sfm_data2 = FileUtils.loadjson(model2.sfm_dataLoc)
if not os.path.isdir(inputImgTmpFolder):
listReconFrameName = [sfm_data2["views"][x]["value"]["ptr_wrapper"]["data"]["filename"] for x in range(0,len(sfm_data2["views"])) if sfm_data2["views"][x]["value"]["ptr_wrapper"]["data"]["id_view"] in model2.reconFrame]
FileUtils.makedir(inputImgTmpFolder)
for reconFrameName in listReconFrameName:
os.system("cp -s " + os.path.join(model2.imgFolLoc,reconFrameName) + " " + inputImgTmpFolder)
# remove all old localization result
FileUtils.removedir(model2.locFolLoc)
FileUtils.makedir(model2.locFolLoc)
# localize the images from model2 on model1
if self.useBow:
os.system(reconIBeaconParam.LOCALIZE_PROJECT_PATH + \
" " + inputImgTmpFolder + \
" " + os.path.dirname(model1.sfm_dataLoc) + \
" " + self.mMatchesPath + \
" " + model2.locFolLoc + \
" -f=" + str(reconParam.locFeatDistRatio) + \
" -r=" + str(reconParam.locRansacRound) + \
" -b=" + model1.beaconFileLoc + \
" -e=" + model2.csvFolLoc + \
" -k=" + str(reconIBeaconParam.locKNNnum) + \
" -c=" + str(reconIBeaconParam.coocThres) + \
" -i=" + str(reconParam.locSkipFrame) + \
" -v=" + str(reconIBeaconParam.locSkipSelKNN) + \
" -n=" + str(reconIBeaconParam.normApproach) + \
" -kb=" + str(reconBOWParam.locKNNnum) + \
" -a=" + os.path.join(self.mMatchesPath, "BOWfile.yml") + \
" -p=" + os.path.join(self.mMatchesPath, "PCAfile.yml"))
else:
os.system(reconIBeaconParam.LOCALIZE_PROJECT_PATH + \
" " + inputImgTmpFolder + \
" " + os.path.dirname(model1.sfm_dataLoc) + \
" " + self.mMatchesPath + \
" " + model2.locFolLoc + \
" -f=" + str(reconParam.locFeatDistRatio) + \
" -r=" + str(reconParam.locRansacRound) + \
" -b=" + model1.beaconFileLoc + \
" -e=" + model2.csvFolLoc + \
" -k=" + str(reconIBeaconParam.locKNNnum) + \
" -c=" + str(reconIBeaconParam.coocThres) + \
" -i=" + str(reconParam.locSkipFrame) + \
" -v=" + str(reconIBeaconParam.locSkipSelKNN) + \
" -n=" + str(reconIBeaconParam.normApproach))
# remove temporary image folder
# removedir(inputImgTmpFolder)
# extract centers from all json file and write to a file
fileLoc = open(os.path.join(model2.locFolLoc,"center.txt"),"w")
countLocFrame = 0
for filename in sorted(os.listdir(model2.locFolLoc)):
if filename[-4:]!="json":
continue
countLocFrame = countLocFrame + 1
with open(os.path.join(model2.locFolLoc,filename)) as locJson:
#print os.path.join(sfm_locOut,filename)
locJsonDict = json.load(locJson)
loc = locJsonDict["t"]
fileLoc.write(str(loc[0]) + " " + str(loc[1]) + " " +str(loc[2]) + " 255 0 0\n" )
fileLoc.close()
# get inlier matches
FileUtils.makedir(sfmOutPath)
resultSfMDataFile = os.path.join(sfmOutPath,"sfm_data.json")
# below also checks if the ratio between first and last svd of M[0:3,0:3]
# is good or not. If not then reject
# TODO : revisit ransacRound parameter, use number of reconstruction frame to determine structure points transform seems small
nMatchPointsTmp, nInlierTmp, M = mergeSfM.mergeModel(model1.sfm_dataLoc,
model2.sfm_dataLoc,
model2.locFolLoc,
resultSfMDataFile,
ransacThres=model1.ransacStructureThres,
mergePointThres=model1.mergeStructureThres,
ransacRoundMul=reconParam.ransacRoundMul,
inputImgDir=self.mInputImgPath,
minLimit=reconParam.min3DnInliers)
ratioInlierMatchPoints = 0.0
if nMatchPointsTmp>0:
ratioInlierMatchPoints = float(nInlierTmp)/nMatchPointsTmp
# 3. perform test whether merge is good
sfm_merge_generated = True
countFileAgree = 0
countFileLoc = 1
if os.path.isfile(resultSfMDataFile):
os.system(reconParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + " " + resultSfMDataFile + " " + resultSfMDataFile)
countFileLoc, countFileAgree = mergeSfM.modelMergeCheckLocal(resultSfMDataFile, model2.locFolLoc, model1.validMergeRansacThres)
else:
sfm_merge_generated = False
ratioAgreeFrameReconFrame = 0.0
if (len(model2.reconFrame)>0):
ratioAgreeFrameReconFrame = float(countFileAgree)/len(model2.reconFrame)
ratioAgreeFrameLocFrame = 0.0
if (countFileLoc>0):
ratioAgreeFrameLocFrame = float(countFileAgree)/countFileLoc
# write log file
with open(os.path.join(self.mSfMPath,"global"+str(self.nMergedModel),"log.txt"),"a") as filelog:
filelog.write(("M1: " + model1.name + "\n" + \
"M2: " + model2.name + "\n" + \
"nMatchedPoints: " + str(nMatchPointsTmp) + "\n" + \
"nInliers: " + str(nInlierTmp) + "\n" + \
"ratioInlierWithMatchedPoints: " + str(ratioInlierMatchPoints) + "\n" + \
"countLocFrame: " + str(countLocFrame) + "\n" + \
"nReconFrame M2: " + str(len(model2.reconFrame)) + "\n" + \
"countFileAgree: " + str(countFileAgree) + "\n" + \
"countFileLoc: " + str(countFileLoc) + "\n" + \
"not sfm_merge_generated: " + str(not sfm_merge_generated) + "\n" + \
# obsolete condition by T. Ishihara 2015.11.10
#"nInlierTmp > "+str(reconParam.vldMergeRatioInliersFileagree)+"*countFileAgree: " + str(nInlierTmp > reconParam.vldMergeRatioInliersFileagree*countFileAgree) + "\n" + \
"countFileAgree > "+str(reconParam.vldMergeMinCountFileAgree)+": " + str(countFileAgree > reconParam.vldMergeMinCountFileAgree) + "\n" + \
# obsolete condition by T. Ishihara 2016.04.02
#"countFileAgree > "+str(reconParam.vldMergeSmallMinCountFileAgree)+": " + str(countFileAgree > reconParam.vldMergeSmallMinCountFileAgree) + "\n" + \
# obsolete condition by T. Ishihara 2016.04.02
#"countFileLoc < countFileAgree*" +str(reconParam.vldMergeShortRatio)+ ": " + str(countFileLoc < countFileAgree*reconParam.vldMergeShortRatio) + "\n" + \
"ratioLocAgreeWithReconFrame: " + str(ratioAgreeFrameReconFrame) + "\n" + \
"ratioLocAgreeWithReconFrame > " + str(reconParam.vldMergeRatioAgrFReconF) + ": " + str(ratioAgreeFrameReconFrame > reconParam.vldMergeRatioAgrFReconF) + "\n" + \
"ratioLocAgreeWithLocFrame: " + str(ratioAgreeFrameLocFrame) + "\n" + \
"ratioLocAgreeWithLocFrame > " + str(reconParam.vldMergeRatioAgrFLocF) + ": " + str(ratioAgreeFrameLocFrame > reconParam.vldMergeRatioAgrFLocF) + "\n" + \
str(M) + "\n\n"))
# rename the localization folder to save localization result
'''
if os.path.isdir(model2.locFolLoc+model1.name):
FileUtils.removedir(model2.locFolLoc+model1.name)
os.rename(model2.locFolLoc,model2.locFolLoc+model1.name)
'''
# obsolete merge condition
'''
if not sfm_merge_generated or \
not (nInlierTmp > reconParam.vldMergeRatioInliersFileagree*countFileAgree and \
((countFileAgree > reconParam.vldMergeMinCountFileAgree or (countFileAgree > reconParam.vldMergeSmallMinCountFileAgree and countFileLoc < countFileAgree*reconParam.vldMergeShortRatio)) and \
((nInlierTmp > reconParam.vldMergeNInliers and float(countFileAgree)/len(model2.reconFrame) > reconParam.vldMergeRatioAgrFReconFNInliers) or float(countFileAgree)/countFileLoc > reconParam.vldMergeRatioAgrFLocF) and
(float(countFileAgree)/len(model2.reconFrame) > reconParam.vldMergeRatioAgrFReconF))):
'''
# update merge condition by T. Ishihara 2015.11.10
'''
if not sfm_merge_generated or \
not (countFileAgree > reconParam.vldMergeMinCountFileAgree and \
countFileAgree > reconParam.vldMergeSmallMinCountFileAgree and \
countFileLoc < countFileAgree*reconParam.vldMergeShortRatio and \
((nInlierTmp > reconParam.vldMergeNInliers and ratioAgreeFrameReconFrame > reconParam.vldMergeRatioAgrFReconFNInliers) or \
ratioAgreeFrameReconFrame > reconParam.vldMergeRatioAgrFReconF) and \
ratioAgreeFrameLocFrame > reconParam.vldMergeRatioAgrFLocF):
'''
# update merge condition by T. Ishihara 2016.04.02
'''
if not sfm_merge_generated or \
not (countFileAgree > reconParam.vldMergeMinCountFileAgree and \
((nInlierTmp > reconParam.vldMergeNInliers and ratioAgreeFrameReconFrame > reconParam.vldMergeRatioAgrFReconFNInliers) or \
ratioAgreeFrameReconFrame > reconParam.vldMergeRatioAgrFReconF) and \
ratioAgreeFrameLocFrame > reconParam.vldMergeRatioAgrFLocF):
'''
# update merge condition by T. Ishihara 2016.06.09
'''
if not sfm_merge_generated or \
not (countFileAgree > reconParam.vldMergeMinCountFileAgree and \
ratioAgreeFrameLocFrame > reconParam.vldMergeRatioAgrFLocF and \
nInlierTmp > reconParam.min3DnInliers and \
ratioInlierMatchPoints > reconParam.vldMergeRatioInliersMatchPoints):
'''
# update merge condition by T. Ishihara 2016.06.20
if not sfm_merge_generated or \
not (countFileAgree > reconParam.vldMergeMinCountFileAgree and \
ratioAgreeFrameLocFrame > reconParam.vldMergeRatioAgrFLocF and \
nInlierTmp > reconParam.min3DnInliers):
print "Transformed locations do not agree with localization. Skip merge between " + model1.name + " and " + model2.name + "."
'''
if os.path.isfile(os.path.join(sfmOutPath,"sfm_data.json")):
os.rename(os.path.join(sfmOutPath,"sfm_data.json"), \
os.path.join(sfmOutPath,"sfm_data_("+model1.name + "," + model2.name+").json"))
'''
# move to next video
return False, sfmModelIBeacon("","","","","","","",validMergeRansacThres=0,validMergeRansacThresK=0,
ransacStructureThres=0, ransacStructureThresK=0,
mergeStructureThres=0, mergeStructureThresK=0)
# generate colorized before bundle adjustment for comparison
os.system("openMVG_main_ComputeSfM_DataColor " +
" -i " + os.path.join(sfmOutPath,"sfm_data.json") +
" -o " + os.path.join(sfmOutPath,"colorized_pre.ply"))
# TODO : try computing structure from know pose here
# https://github.com/openMVG/openMVG/issues/246
# http://openmvg.readthedocs.io/en/latest/software/SfM/ComputeStructureFromKnownPoses/
# TODO : revisit the order of bundle adjustment
# perform bundle adjustment
'''
os.system(reconParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + " " + os.path.join(sfmOutPath,"sfm_data.json") + " " + os.path.join(sfmOutPath,"sfm_data.json") + \
" -c=" + "rs,rst,rsti" + " -r=" + "1")
'''
os.system(reconParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + " " + os.path.join(sfmOutPath,"sfm_data.json") + " " + os.path.join(sfmOutPath,"sfm_data.json") + \
" -c=" + "rst,rsti" + " -r=" + "1")
os.system("openMVG_main_ComputeSfM_DataColor " +
" -i " + os.path.join(sfmOutPath,"sfm_data.json") +
" -o " + os.path.join(sfmOutPath,"colorized.ply"))
# write new beacon file
IBeaconUtils.exportBeaconDataForSfmImageFrames(self.mCsvPath, resultSfMDataFile, os.path.join(self.mInputPath,"listbeacon.txt"),
os.path.join(sfmOutPath,"beacon.txt"), reconIBeaconParam.normApproach)
return True, sfmModelIBeacon("A" + model1.name + "," + model2.name +"Z", self.mInputImgPath, self.mCsvPath,
os.path.join(sfmOutPath,"beacon.txt"), self.mMatchesPath, os.path.join(sfmOutPath,"loc"),
resultSfMDataFile, validMergeRansacThres=model1.validMergeRansacThres,
ransacStructureThres=model1.ransacStructureThres,
mergeStructureThres=model1.mergeStructureThres)
def main():
# set parameter
reconstructParam = ReconstructParam.ReconstructParam
reconstructIBeaconParam = ReconstructIBeaconParam.ReconstructIBeaconParam
reconstructBOWParam = ReconstructBOWParam.ReconstructBOWParam
# parse parameters
description = 'This script is for reconstruct 3D models from multiple videos and merge to one 3D model. ' + \
'iBeacon is used for accelerating 3D model merge. ' + \
'Please prepare multiple videos in Input folder.'
parser = argparse.ArgumentParser(description=description)
parser.add_argument('project_path', action='store', nargs=None, const=None, \
default=None, type=str, choices=None, metavar=None, \
help='Directory path where your 3D model project is stored.')
parser.add_argument('-k', '--path-camera-file', action='store', nargs='?', const=None, \
default=None, type=str, choices=None, metavar=None, \
help='File path where camera matrix is stored in Numpy text format. (default: focal length ' + \
str(reconstructParam.focalLength) + ' will be used)')
parser.add_argument('--bow', action='store_true', default=False, \
help='Use BOW to accelerate 3D model merge if this flag is set (default: False)')
args = parser.parse_args()
PROJECT_PATH = args.project_path
USE_BOW = args.bow
PATH_CAMERA_FILE = args.path_camera_file
if PATH_CAMERA_FILE:
if os.path.exists(PATH_CAMERA_FILE):
with open(PATH_CAMERA_FILE,"r") as camMatFile:
K = np.loadtxt(camMatFile)
if K.shape[0]!=3 or K.shape[1]!=3:
print "Error : invalid camera matrix size = " + str(K)
sys.exit()
print "Focal length " + str(K[0][0]) + " is set for reconstruction"
reconstructParam.focalLength = K[0][0]
else:
print "Error : invalid camera matrix file = " + PATH_CAMERA_FILE
sys.exit()
# get paths
inputPath = os.path.join(PROJECT_PATH, "Input")
outputPath = os.path.join(PROJECT_PATH, "Output")
FileUtils.makedir(outputPath)
# reconstruct all videos
listVideo = sorted(os.listdir(inputPath))
for video in listVideo:
if not os.path.isdir(os.path.join(inputPath, video)):
continue
print "Begin reconstructing video : " + video
sfm_mainDir = os.path.join(outputPath, video)
sfm_inputDir = os.path.join(inputPath, video)
sfm_inputImgDir = os.path.join(sfm_inputDir, "inputImg")
sfm_matchesDir = os.path.join(sfm_mainDir, "matches")
sfm_sfmDir = os.path.join(sfm_mainDir, "SfM")
sfm_reconstructDir = os.path.join(sfm_sfmDir, "reconstruction")
sfm_globalDir = os.path.join(sfm_reconstructDir, "global")
FileUtils.makedir(sfm_mainDir)
FileUtils.makedir(sfm_inputImgDir)
FileUtils.makedir(sfm_matchesDir)
FileUtils.makedir(sfm_sfmDir)
FileUtils.makedir(sfm_reconstructDir)
FileUtils.makedir(sfm_globalDir)
if not os.path.isfile(os.path.join(sfm_globalDir, "sfm_data.json")):
# list images
os.system("openMVG_main_SfMInit_ImageListing -i " + sfm_inputImgDir + " -o " + sfm_matchesDir + " -d " + reconstructParam.CAMERA_DATABASE_PATH)
# 1.1 Check intrinsic
# ( if camera parameter not specified then replace with fixed camera.
# and set appropriate width and height)
with open(os.path.join(sfm_matchesDir, "sfm_data.json")) as sfm_data_file:
sfm_data = json.load(sfm_data_file)
hImg = sfm_data["views"][0]['value']['ptr_wrapper']['data']["height"]
wImg = sfm_data["views"][0]['value']['ptr_wrapper']['data']["width"]
if len(sfm_data["intrinsics"]) == 0:
for view in sfm_data["views"]:
view["value"]["ptr_wrapper"]["data"]["id_intrinsic"] = 0;
sfm_data["intrinsics"].append({})
sfm_data["intrinsics"][0]["key"] = 0
sfm_data["intrinsics"][0]["values"] = {}
# sfm_data["intrinsics"][0]["values"]["polymorphic_name"] = "pinhole_radial_k3"
sfm_data["intrinsics"][0]["values"]["polymorphic_name"] = "pinhole"
sfm_data["intrinsics"][0]["values"]["polymorphic_id"] = 2147999999
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"] = {}
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["id"] = 2147483660
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"] = {}
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"]["width"] = wImg
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"]["height"] = hImg
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"]["focal_length"] = reconstructParam.focalLength
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"]["disto_k3"] = [0, 0, 0]
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"]["principal_point"] = [wImg / 2, hImg / 2]
with open(os.path.join(sfm_matchesDir, "sfm_data.json"), "w") as sfm_data_file:
json.dump(sfm_data, sfm_data_file)
# 2 - Features computation and matching
# ( Compute per image a list of features & descriptors )
IBeaconUtils.exportBeaconDataForSfmImageFrames(os.path.join(sfm_inputDir, "csv"), os.path.join(sfm_matchesDir, "sfm_data.json"),
os.path.join(inputPath, "listbeacon.txt"), os.path.join(sfm_matchesDir, "beacon.txt"),
reconstructIBeaconParam.normApproach)
os.system(reconstructParam.EXTRACT_FEATURE_MATCH_PROJECT_PATH + \
" " + sfm_matchesDir + \
" -mf=" + str(reconstructParam.maxTrackletMatchDistance) + \
" -mm=" + str(reconstructParam.minMatchToRetain) + \
" -f=" + str(reconstructParam.extFeatDistRatio) + \
" -r=" + str(reconstructParam.extFeatRansacRound))
# OpenMVG assumes matches.e.txt for global reconstruction, matches.f.txt for incremental reconstruction
os.system("cp " + os.path.join(sfm_matchesDir, "matches.f.txt") + " " + os.path.join(sfm_matchesDir, "matches.e.txt"))
# 3 - Global reconstruction
countRecon = 1
while not os.path.isfile(os.path.join(sfm_globalDir, "sfm_data.json")) and countRecon < reconstructParam.rerunRecon:
os.system("openMVG_main_GlobalSfM -i " + os.path.join(sfm_matchesDir, "sfm_data.json") + " -m " + sfm_matchesDir + " -o " + sfm_globalDir)
# for OpenMVG 1.0
#os.system("openMVG_main_ConvertSfM_DataFormat -i " + os.path.join(sfm_globalDir, "sfm_data.bin") + " -o " + os.path.join(sfm_globalDir, "sfm_data.json"))
countRecon = countRecon + 1
time.sleep(1)
if not os.path.isfile(os.path.join(sfm_globalDir, "sfm_data.json")):
continue
# 4 - Color the pointcloud
os.system("openMVG_main_ComputeSfM_DataColor -i " + os.path.join(sfm_globalDir, "sfm_data.json") + " -o " + os.path.join(sfm_globalDir, "colorized.ply"))
# 4.5 remove part of reconstruction where it is incorrect
# Specifically, sometimes when their matching is not adequate,
# the reconstructed model will be divided into two or more models
# with different scale and a "jump" between pose translation.
# This function detects such jump and retain the the largest
# beginning or ending part of reconstruction, while the rest
# should be reconstructed separately by cleanSfM.
countCut = 0
# keep cutting until no more cut
while cleanSfM.cutSfMDataJump(os.path.join(sfm_globalDir, "sfm_data.json"), bufferFrame=reconstructParam.bufferFrame):
countCut = countCut + 1
os.rename(os.path.join(sfm_globalDir, "sfm_data_BC.json"),
os.path.join(sfm_globalDir, "sfm_data_BC" + str(countCut) + ".json"))
# modified by T. Ishihara 2016.06.14
# change bundle adjustment parameter for cutted model
'''
os.system(reconstructParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + \
" " + os.path.join(sfm_globalDir, "sfm_data.json") + \
" " + os.path.join(sfm_globalDir, "sfm_data.json") + \
" -c=" + "rs,rst,rsti")
'''
os.system(reconstructParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + \
" " + os.path.join(sfm_globalDir, "sfm_data.json") + \
" " + os.path.join(sfm_globalDir, "sfm_data.json") + \
" -c=" + "rst,rsti" + " -r=" + "1")
os.system("openMVG_main_ComputeSfM_DataColor -i " + os.path.join(sfm_globalDir, "sfm_data.json") + " -o " + os.path.join(sfm_globalDir, "colorized_AC.ply"))
# 5 - Clean sfm_data by removing viewID of frames that are not used
# in reconstruction and put them in another folder and reconstruct them again
# note that sfm_data.json in matches folder is renamed and kept as reference
unusedImg = cleanSfM.cleanSfM(os.path.join(sfm_globalDir, "sfm_data.json"),
[os.path.join(sfm_matchesDir, "matches.putative.txt"),
os.path.join(sfm_matchesDir, "matches.e.txt"),
os.path.join(sfm_matchesDir, "matches.f.txt")])
# 6. move unused images, csv files into a new folder unless they have less than x images
for i in range(0, len(unusedImg)):
listUnused = unusedImg[i]
if len(listUnused) < reconstructParam.minUnusedImgLength:
continue
# set name for new video
if i == 0:
newVidName = video + "_front"
elif i == 1:
newVidName = video + "_back"
else:
# this should not be called
continue
# set path
pathNewVid = os.path.join(inputPath, newVidName)
# skip if there is already this folder
if os.path.isdir(pathNewVid):
continue
print "Extract unused part of " + video + " into " + newVidName
FileUtils.makedir(pathNewVid)
csvNewVid = os.path.join(pathNewVid, "csv")
imgNewVid = os.path.join(pathNewVid, "inputImg")
FileUtils.makedir(csvNewVid)
FileUtils.makedir(imgNewVid)
# copy image in list and csv file
os.system("cp -s " + os.path.join(sfm_inputDir, "csv", "*.csv") + " " + csvNewVid)
for unusedFilename in listUnused:
os.system("cp -s " + os.path.join(sfm_inputImgDir, unusedFilename) + " " + imgNewVid)
# append the folder into reconstruction queue
listVideo.append(newVidName)
# train bag of words model, and extract bag of words feature for all images
if USE_BOW and not os.path.isfile(os.path.join(outputPath, "merge_result", "Output", "matches", "BOWfile.yml")):
outputBowPath = os.path.join(outputPath, "merge_result", "Output", "matches")
if not os.path.isdir(outputBowPath):
FileUtils.makedir(outputBowPath)
print "Execute Training BOW : " + reconstructParam.WORKSPACE_DIR + "/TrainBoW/Release/TrainBoW " + outputPath + " " + \
os.path.join(outputBowPath, "BOWfile.yml") + " -p=" + os.path.join(outputBowPath, "PCAfile.yml")
os.system(reconstructParam.WORKSPACE_DIR + "/TrainBoW/Release/TrainBoW " + outputPath + " " + \
os.path.join(outputBowPath, "BOWfile.yml") + " -p=" + os.path.join(outputBowPath, "PCAfile.yml"))
# load graph structure from "mergeGraph.txt" if it exists
# create new graph structure if it does not exist
if os.path.isfile(os.path.join(outputPath, "merge_result", "Output", "SfM", "reconstruction", "mergeGraph.txt")):
sfmGraph = sfmMergeGraphIBeacon.sfmGraphIBeacon.load(os.path.join(outputPath, "merge_result", "Output", "SfM", "reconstruction", "mergeGraph.txt"))
sfmGraph.workspacePath = reconstructParam.WORKSPACE_DIR
#### start of manually adding new model code ####
# In current code, you cannot add new 3D model once you start merging.
# Enable following commented code to add new 3D model after you already started merging.
'''
newModelToAdd = []
for newModelName in newModelToAdd:
addModel(newModelName,os.path.join(inputPath,newModelName),os.path.join(outputPath,newModelName))
sfmGraph.clearBadMatches()
'''
### end of manually adding new model code ###
else:
sfmGraph = sfmMergeGraphIBeacon.sfmGraphIBeacon(inputPath,
outputPath,
os.path.join(outputPath, "merge_result", "Input"),
os.path.join(outputPath, "merge_result", "Output", "SfM", "reconstruction"),
os.path.join(outputPath, "merge_result", "Output", "matches"),
os.path.join(outputPath, "merge_result", "Input", "csv"),
os.path.join(outputPath, "merge_result", "Input", "inputImg"),
reconstructParam.WORKSPACE_DIR,
USE_BOW,
validMergeRansacThresK=reconstructParam.vldMergeAgrFrameThresK,
minReconFrame=reconstructParam.minReconFrameToAdd)
sfmGraph.mergeModel(os.path.join(inputPath, "listbeacon.txt"),
os.path.join(outputPath, listVideo[0], "matches", "image_describer.txt"),
inputPath,
outputPath,
reconParam=reconstructParam,
reconIBeaconParam=reconstructIBeaconParam,
reconBOWParam=reconstructBOWParam)
# select largest 3D model and save it
SfMDataUtils.saveFinalSfM(PROJECT_PATH)
def main():
description = 'This script is for merging multiple SfM output models to one SfM model.' + \
'Please prepare multiple OpenMVG projects which have output SfM models, and matrix to convert to global coordinate.'
parser = argparse.ArgumentParser(description=description)
parser.add_argument('input_csv', action='store', nargs=None, const=None, \
default=None, type=str, choices=None, metavar=None, \
help='Input CSV file which lists OpenMVG projects which will be merged.')
parser.add_argument('output_dir', action='store', nargs=None, const=None, \
default=None, type=str, choices=None, metavar=None, \
help='Output directory path where merged model will be saved.')
args = parser.parse_args()
input_csv = args.input_csv
output_dir = args.output_dir
# load reconstruct parameters
reconstructParam = ReconstructParam.ReconstructParam
reconstructIBeaconParam = ReconstructIBeaconParam.ReconstructIBeaconParam
# read projects list
projectList = []
with open(input_csv, "r") as f:
reader = csv.reader(f)
for row in reader:
project = {}
project["dir"] = row[0]
project["sfm_data"] = row[1]
project["A"] = row[2]
projectList.append(project)
# copy source files to output directory
for project in projectList:
copyOriginalFiles(project["dir"], output_dir)
# load beacon settings
mergeBeaconmap = None
for project in projectList:
beacon_file = os.path.join(project["dir"], "Input", "listbeacon.txt")
if os.path.exists(beacon_file):
beaconmap = iBeaconUtils.parseBeaconSetting(beacon_file)
if mergeBeaconmap is None:
mergeBeaconmap = beaconmap
else:
if mergeBeaconmap!=beaconmap:
print "invalid find listbeacon.txt for project data : " + project["dir"]
print "listbeacon.txt should be same for all project data"
sys.exit()
else:
print "valid listbeacon.txt for project data : " + project["dir"]
# prepare output directory
if not os.path.isdir(os.path.join(output_dir,"Ref")):
FileUtils.makedir(os.path.join(output_dir,"Ref"))
if not os.path.isdir(os.path.join(output_dir,"Ref","loc")):
FileUtils.makedir(os.path.join(output_dir,"Ref","loc"))
if not os.path.isdir(os.path.join(output_dir,"Output","SfM")):
FileUtils.makedir(os.path.join(output_dir,"Output","SfM"))
if not os.path.isdir(os.path.join(output_dir,"Output","SfM","reconstruction")):
FileUtils.makedir(os.path.join(output_dir,"Output","SfM","reconstruction"))
if not os.path.isdir(os.path.join(output_dir,"Output","SfM","reconstruction","global")):
FileUtils.makedir(os.path.join(output_dir,"Output","SfM","reconstruction","global"))
sfmDataList = []
for project in projectList:
if not os.path.exists(project["sfm_data"]):
print "cannot find sfm data : " + project["sfm_data"]
sys.exit()
with open(project["sfm_data"]) as jsonFile:
sfmDataList.append(json.load(jsonFile))
AList = []
for project in projectList:
AList.append(np.loadtxt(project["A"]))
print "load mat : " + project["A"]
print (np.loadtxt(project["A"]))
print "Load 3D points"
pointIdList = []
pointList = []
for sfmData in sfmDataList:
pointId, point = mergeSfM.getAll3DPointloc(sfmData)
pointn = np.asarray(point, dtype=np.float).T
pointIdList.append(pointId)
pointList.append(pointn)
# merge models
mergeSfmData = None
mergePointId = None
mergePointn = None
for idx in range(0, len(sfmDataList)):
if idx==0:
mergeSfmData = sfmDataList[0]
mergeSfM.transform_sfm_data(mergeSfmData, AList[0])
else:
mergePointThres = mergeSfM.findMedianStructurePointsThres(mergeSfmData, reconstructParam.mergePointThresMul)
print "thres to merge 3D points : " + str(mergePointThres)
inlierMap = findInliersByKnownTransform(mergePointId, pointIdList[idx], mergePointn, pointList[idx], AList[idx], mergePointThres)
print "number of points in base model : " + str(len(mergePointn[0]))
print "number of points in model " + str(idx) + " : " + str(len(pointList[idx]))
print "number of inliers : " + str(len(inlierMap))
mergeSfM.merge_sfm_data(mergeSfmData, sfmDataList[idx], AList[idx], {x[0]: x[1] for x in inlierMap})
mergePointId, mergePoint = mergeSfM.getAll3DPointloc(mergeSfmData)
mergePointn = np.asarray(mergePoint, dtype=np.float).T
# go back to coordinate of the first model
_invA = np.linalg.inv(AList[0][0:3,0:3])
invA = np.c_[_invA, -np.dot(_invA,AList[0][:,3])]
mergeSfM.transform_sfm_data(mergeSfmData, invA)
mergeSfmData["root_path"] = os.path.join(output_dir,"Input","inputImg")
resultSfMDataFile = os.path.join(output_dir,"Output","SfM","reconstruction","global","sfm_data.json")
with open(resultSfMDataFile,"w") as jsonfile:
json.dump(mergeSfmData, jsonfile)
# write new beacon file
if mergeBeaconmap is not None:
iBeaconUtils.exportBeaconDataForSfmImageFrames(os.path.join(output_dir,"Input","csv"), resultSfMDataFile,
os.path.join(output_dir,"Input","listbeacon.txt"),
os.path.join(output_dir,"Output","SfM","reconstruction","global","beacon.txt"),
reconstructIBeaconParam.normApproach)
'''
os.system(reconstructParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + " " + resultSfMDataFile + " " + resultSfMDataFile)
'''
os.system(reconstructParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + " " + resultSfMDataFile + " " + resultSfMDataFile + \
" -c=" + "rst,rsti" + " -r=" + "1")
Amat = AList[0]
with open(os.path.join(output_dir,"Ref","Amat.txt"),"w") as AmatFile:
np.savetxt(AmatFile,Amat)
FileUtils.convertNumpyMatTxt2OpenCvMatYml(os.path.join(output_dir,"Ref","Amat.txt"), os.path.join(output_dir,"Ref","Amat.yml"), "A")
# To create same directory structure before merging, create sfm_data.json without structure information in matches directory
with open(resultSfMDataFile) as fpr:
sfmData = json.load(fpr)
sfmData["extrinsics"] = []
sfmData["control_points"] = []
sfmData["structure"] = []
with open(os.path.join(output_dir,"Output","matches","sfm_data.json"),"w") as fpw:
json.dump(sfmData, fpw)
print "Execute : " + reconstructParam.WORKSPACE_DIR + "/TrainBoW/Release/TrainBoW " + os.path.join(output_dir,"Output") + " " + \
os.path.join(output_dir,"Output", "matches", "BOWfile.yml") + " -p=" + os.path.join(output_dir,"Output", "matches", "PCAfile.yml")
os.system(reconstructParam.WORKSPACE_DIR + "/TrainBoW/Release/TrainBoW " + os.path.join(output_dir,"Output") + " " + \
os.path.join(output_dir,"Output", "matches", "BOWfile.yml") + " -p=" + os.path.join(output_dir,"Output", "matches", "PCAfile.yml"))
os.system("openMVG_main_ComputeSfM_DataColor -i " + resultSfMDataFile + \
" -o " + os.path.join(output_dir,"Output","SfM","reconstruction","global","colorized.ply"))
def main():
# set parameter
reconstructParam = ReconstructParam.ReconstructParam
reconstructIBeaconParam = ReconstructIBeaconParam.ReconstructIBeaconParam
reconstructBOWParam = ReconstructBOWParam.ReconstructBOWParam
# parse parameters
description = 'This script is for reconstruct 3D models from multiple videos and merge to one 3D model. ' + \
'iBeacon is used for accelerating 3D model merge. ' + \
'Please prepare multiple videos in Input folder.'
parser = argparse.ArgumentParser(description=description)
parser.add_argument('project_path', action='store', nargs=None, const=None, \
default=None, type=str, choices=None, metavar=None, \
help='Directory path where your 3D model project is stored.')
parser.add_argument('-k', '--path-camera-file', action='store', nargs='?', const=None, \
default=None, type=str, choices=None, metavar=None, \
help='File path where camera matrix is stored in Numpy text format. (default: focal length ' + \
str(reconstructParam.focalLength) + ' will be used)')
parser.add_argument('--bow', action='store_true', default=False, \
help='Use BOW to accelerate 3D model merge if this flag is set (default: False)')
args = parser.parse_args()
PROJECT_PATH = args.project_path
USE_BOW = args.bow
PATH_CAMERA_FILE = args.path_camera_file
if PATH_CAMERA_FILE:
if os.path.exists(PATH_CAMERA_FILE):
with open(PATH_CAMERA_FILE, "r") as camMatFile:
K = np.loadtxt(camMatFile)
if K.shape[0] != 3 or K.shape[1] != 3:
print "Error : invalid camera matrix size = " + str(K)
sys.exit()
print "Focal length " + str(K[0][0]) + " is set for reconstruction"
reconstructParam.focalLength = K[0][0]
else:
print "Error : invalid camera matrix file = " + PATH_CAMERA_FILE
sys.exit()
# get paths
inputPath = os.path.join(PROJECT_PATH, "Input")
outputPath = os.path.join(PROJECT_PATH, "Output")
FileUtils.makedir(outputPath)
# reconstruct all videos
listVideo = sorted(os.listdir(inputPath))
for video in listVideo:
if not os.path.isdir(os.path.join(inputPath, video)):
continue
print "Begin reconstructing video : " + video
sfm_mainDir = os.path.join(outputPath, video)
sfm_inputDir = os.path.join(inputPath, video)
sfm_inputImgDir = os.path.join(sfm_inputDir, "inputImg")
sfm_matchesDir = os.path.join(sfm_mainDir, "matches")
sfm_sfmDir = os.path.join(sfm_mainDir, "SfM")
sfm_reconstructDir = os.path.join(sfm_sfmDir, "reconstruction")
sfm_globalDir = os.path.join(sfm_reconstructDir, "global")
FileUtils.makedir(sfm_mainDir)
FileUtils.makedir(sfm_inputImgDir)
FileUtils.makedir(sfm_matchesDir)
FileUtils.makedir(sfm_sfmDir)
FileUtils.makedir(sfm_reconstructDir)
FileUtils.makedir(sfm_globalDir)
if not os.path.isfile(os.path.join(sfm_globalDir, "sfm_data.json")):
# list images
os.system("openMVG_main_SfMInit_ImageListing -i " +
sfm_inputImgDir + " -o " + sfm_matchesDir + " -d " +
reconstructParam.CAMERA_DATABASE_PATH)
# 1.1 Check intrinsic
# ( if camera parameter not specified then replace with fixed camera.
# and set appropriate width and height)
with open(os.path.join(sfm_matchesDir,
"sfm_data.json")) as sfm_data_file:
sfm_data = json.load(sfm_data_file)
hImg = sfm_data["views"][0]['value']['ptr_wrapper']['data'][
"height"]
wImg = sfm_data["views"][0]['value']['ptr_wrapper']['data'][
"width"]
if len(sfm_data["intrinsics"]) == 0:
for view in sfm_data["views"]:
view["value"]["ptr_wrapper"]["data"][
"id_intrinsic"] = 0
sfm_data["intrinsics"].append({})
sfm_data["intrinsics"][0]["key"] = 0
sfm_data["intrinsics"][0]["values"] = {}
# sfm_data["intrinsics"][0]["values"]["polymorphic_name"] = "pinhole_radial_k3"
sfm_data["intrinsics"][0]["values"][
"polymorphic_name"] = "pinhole"
sfm_data["intrinsics"][0]["values"][
"polymorphic_id"] = 2147999999
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"] = {}
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"][
"id"] = 2147483660
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"][
"data"] = {}
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"][
"width"] = wImg
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"][
"height"] = hImg
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"][
"focal_length"] = reconstructParam.focalLength
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"][
"disto_k3"] = [0, 0, 0]
sfm_data["intrinsics"][0]["values"]["ptr_wrapper"]["data"][
"principal_point"] = [wImg / 2, hImg / 2]
with open(os.path.join(sfm_matchesDir, "sfm_data.json"),
"w") as sfm_data_file:
json.dump(sfm_data, sfm_data_file)
# 2 - Features computation and matching
# ( Compute per image a list of features & descriptors )
IBeaconUtils.exportBeaconDataForSfmImageFrames(
os.path.join(sfm_inputDir, "csv"),
os.path.join(sfm_matchesDir, "sfm_data.json"),
os.path.join(inputPath, "listbeacon.txt"),
os.path.join(sfm_matchesDir, "beacon.txt"),
reconstructIBeaconParam.normApproach)
os.system(reconstructParam.EXTRACT_FEATURE_MATCH_PROJECT_PATH + \
" " + sfm_matchesDir + \
" -mf=" + str(reconstructParam.maxTrackletMatchDistance) + \
" -mm=" + str(reconstructParam.minMatchToRetain) + \
" -f=" + str(reconstructParam.extFeatDistRatio) + \
" -r=" + str(reconstructParam.extFeatRansacRound))
# OpenMVG assumes matches.e.txt for global reconstruction, matches.f.txt for incremental reconstruction
os.system("cp " + os.path.join(sfm_matchesDir, "matches.f.txt") +
" " + os.path.join(sfm_matchesDir, "matches.e.txt"))
# 3 - Global reconstruction
countRecon = 1
while not os.path.isfile(
os.path.join(sfm_globalDir, "sfm_data.json")
) and countRecon < reconstructParam.rerunRecon:
os.system("openMVG_main_GlobalSfM -i " +
os.path.join(sfm_matchesDir, "sfm_data.json") +
" -m " + sfm_matchesDir + " -o " + sfm_globalDir)
# for OpenMVG 1.0
#os.system("openMVG_main_ConvertSfM_DataFormat -i " + os.path.join(sfm_globalDir, "sfm_data.bin") + " -o " + os.path.join(sfm_globalDir, "sfm_data.json"))
countRecon = countRecon + 1
time.sleep(1)
if not os.path.isfile(os.path.join(sfm_globalDir,
"sfm_data.json")):
continue
# 4 - Color the pointcloud
os.system("openMVG_main_ComputeSfM_DataColor -i " +
os.path.join(sfm_globalDir, "sfm_data.json") + " -o " +
os.path.join(sfm_globalDir, "colorized.ply"))
# 4.5 remove part of reconstruction where it is incorrect
# Specifically, sometimes when their matching is not adequate,
# the reconstructed model will be divided into two or more models
# with different scale and a "jump" between pose translation.
# This function detects such jump and retain the the largest
# beginning or ending part of reconstruction, while the rest
# should be reconstructed separately by cleanSfM.
countCut = 0
# keep cutting until no more cut
while cleanSfM.cutSfMDataJump(
os.path.join(sfm_globalDir, "sfm_data.json"),
bufferFrame=reconstructParam.bufferFrame):
countCut = countCut + 1
os.rename(
os.path.join(sfm_globalDir, "sfm_data_BC.json"),
os.path.join(sfm_globalDir,
"sfm_data_BC" + str(countCut) + ".json"))
# modified by T. Ishihara 2016.06.14
# change bundle adjustment parameter for cutted model
'''
os.system(reconstructParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + \
" " + os.path.join(sfm_globalDir, "sfm_data.json") + \
" " + os.path.join(sfm_globalDir, "sfm_data.json") + \
" -c=" + "rs,rst,rsti")
'''
os.system(reconstructParam.BUNDLE_ADJUSTMENT_PROJECT_PATH + \
" " + os.path.join(sfm_globalDir, "sfm_data.json") + \
" " + os.path.join(sfm_globalDir, "sfm_data.json") + \
" -c=" + "rst,rsti" + " -r=" + "1")
os.system("openMVG_main_ComputeSfM_DataColor -i " +
os.path.join(sfm_globalDir, "sfm_data.json") + " -o " +
os.path.join(sfm_globalDir, "colorized_AC.ply"))
# 5 - Clean sfm_data by removing viewID of frames that are not used
# in reconstruction and put them in another folder and reconstruct them again
# note that sfm_data.json in matches folder is renamed and kept as reference
unusedImg = cleanSfM.cleanSfM(
os.path.join(sfm_globalDir, "sfm_data.json"), [
os.path.join(sfm_matchesDir, "matches.putative.txt"),
os.path.join(sfm_matchesDir, "matches.e.txt"),
os.path.join(sfm_matchesDir, "matches.f.txt")
])
# 6. move unused images, csv files into a new folder unless they have less than x images
for i in range(0, len(unusedImg)):
listUnused = unusedImg[i]
if len(listUnused) < reconstructParam.minUnusedImgLength:
continue
# set name for new video
if i == 0:
newVidName = video + "_front"
elif i == 1:
newVidName = video + "_back"
else:
# this should not be called
continue
# set path
pathNewVid = os.path.join(inputPath, newVidName)
# skip if there is already this folder
if os.path.isdir(pathNewVid):
continue
print "Extract unused part of " + video + " into " + newVidName
FileUtils.makedir(pathNewVid)
csvNewVid = os.path.join(pathNewVid, "csv")
imgNewVid = os.path.join(pathNewVid, "inputImg")
FileUtils.makedir(csvNewVid)
FileUtils.makedir(imgNewVid)
# copy image in list and csv file
os.system("cp -s " +
os.path.join(sfm_inputDir, "csv", "*.csv") + " " +
csvNewVid)
for unusedFilename in listUnused:
os.system("cp -s " +
os.path.join(sfm_inputImgDir, unusedFilename) +
" " + imgNewVid)
# append the folder into reconstruction queue
listVideo.append(newVidName)
# train bag of words model, and extract bag of words feature for all images
if USE_BOW and not os.path.isfile(
os.path.join(outputPath, "merge_result", "Output", "matches",
"BOWfile.yml")):
outputBowPath = os.path.join(outputPath, "merge_result", "Output",
"matches")
if not os.path.isdir(outputBowPath):
FileUtils.makedir(outputBowPath)
print "Execute Training BOW : " + reconstructParam.WORKSPACE_DIR + "/TrainBoW/Release/TrainBoW " + outputPath + " " + \
os.path.join(outputBowPath, "BOWfile.yml") + " -p=" + os.path.join(outputBowPath, "PCAfile.yml")
os.system(reconstructParam.WORKSPACE_DIR + "/TrainBoW/Release/TrainBoW " + outputPath + " " + \
os.path.join(outputBowPath, "BOWfile.yml") + " -p=" + os.path.join(outputBowPath, "PCAfile.yml"))
# load graph structure from "mergeGraph.txt" if it exists
# create new graph structure if it does not exist
if os.path.isfile(
os.path.join(outputPath, "merge_result", "Output", "SfM",
"reconstruction", "mergeGraph.txt")):
sfmGraph = sfmMergeGraphIBeacon.sfmGraphIBeacon.load(
os.path.join(outputPath, "merge_result", "Output", "SfM",
"reconstruction", "mergeGraph.txt"))
sfmGraph.workspacePath = reconstructParam.WORKSPACE_DIR
#### start of manually adding new model code ####
# In current code, you cannot add new 3D model once you start merging.
# Enable following commented code to add new 3D model after you already started merging.
'''
newModelToAdd = []
for newModelName in newModelToAdd:
addModel(newModelName,os.path.join(inputPath,newModelName),os.path.join(outputPath,newModelName))
sfmGraph.clearBadMatches()
'''
### end of manually adding new model code ###
else:
sfmGraph = sfmMergeGraphIBeacon.sfmGraphIBeacon(
inputPath,
outputPath,
os.path.join(outputPath, "merge_result", "Input"),
os.path.join(outputPath, "merge_result", "Output", "SfM",
"reconstruction"),
os.path.join(outputPath, "merge_result", "Output", "matches"),
os.path.join(outputPath, "merge_result", "Input", "csv"),
os.path.join(outputPath, "merge_result", "Input", "inputImg"),
reconstructParam.WORKSPACE_DIR,
USE_BOW,
validMergeRansacThresK=reconstructParam.vldMergeAgrFrameThresK,
minReconFrame=reconstructParam.minReconFrameToAdd)
sfmGraph.mergeModel(os.path.join(inputPath, "listbeacon.txt"),
os.path.join(outputPath, listVideo[0], "matches",
"image_describer.txt"),
inputPath,
outputPath,
reconParam=reconstructParam,
reconIBeaconParam=reconstructIBeaconParam,
reconBOWParam=reconstructBOWParam)
# select largest 3D model and save it
SfMDataUtils.saveFinalSfM(PROJECT_PATH)