def main():
# set default parameter
reconstructParam = ReconstructParam.ReconstructParam
reconstructBOWParam = ReconstructBOWParam.ReconstructBOWParam
# parse parameters
description = 'This script is for reconstruct 3D models from multiple videos and merge to one 3D model. ' + \
'BOW 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 a list of features & descriptors for each image)
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)
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"))
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("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")):
if USE_BOW:
sfmGraph = sfmMergeGraphBOW.sfmGraphBOW.load(os.path.join(outputPath, "merge_result", "Output", "SfM", "reconstruction", "mergeGraph.txt"))
else:
sfmGraph = sfmMergeGraph.sfmGraph.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:
if USE_BOW:
sfmGraph = sfmMergeGraphBOW.sfmGraphBOW(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,
reconstructParam.minReconFrameToAdd)
else:
sfmGraph = sfmMergeGraph.sfmGraph(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,
reconstructParam.minReconFrameToAdd)
if USE_BOW:
sfmGraph.mergeModel(os.path.join(outputPath, listVideo[0], "matches", "image_describer.txt"),
inputPath,
outputPath,
reconParam=reconstructParam,
reconBOWParam=reconstructBOWParam)
else:
sfmGraph.mergeModel(os.path.join(outputPath, listVideo[0], "matches", "image_describer.txt"),
inputPath,
outputPath,
reconParam=reconstructParam)
# select largest 3D model and save it
SfMDataUtils.saveFinalSfM(PROJECT_PATH)
def main():
# set default parameter
reconstructParam = ReconstructParam.ReconstructParam
reconstructBOWParam = ReconstructBOWParam.ReconstructBOWParam
# parse parameters
description = 'This script is for reconstruct 3D models from multiple videos and merge to one 3D model. ' + \
'BOW 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 a list of features & descriptors for each image)
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)
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"))
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("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")):
if USE_BOW:
sfmGraph = sfmMergeGraphBOW.sfmGraphBOW.load(
os.path.join(outputPath, "merge_result", "Output", "SfM",
"reconstruction", "mergeGraph.txt"))
else:
sfmGraph = sfmMergeGraph.sfmGraph.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:
if USE_BOW:
sfmGraph = sfmMergeGraphBOW.sfmGraphBOW(
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,
reconstructParam.minReconFrameToAdd)
else:
sfmGraph = sfmMergeGraph.sfmGraph(
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,
reconstructParam.minReconFrameToAdd)
if USE_BOW:
sfmGraph.mergeModel(os.path.join(outputPath, listVideo[0], "matches",
"image_describer.txt"),
inputPath,
outputPath,
reconParam=reconstructParam,
reconBOWParam=reconstructBOWParam)
else:
sfmGraph.mergeModel(os.path.join(outputPath, listVideo[0], "matches",
"image_describer.txt"),
inputPath,
outputPath,
reconParam=reconstructParam)
# select largest 3D model and save it
SfMDataUtils.saveFinalSfM(PROJECT_PATH)
