predictor.writeTracks()
elif options.choice==2:
print "### \n # \n ###\n We are going to compute features from existing trajectories for plate {}\n Adding density information".format(options.plate)
try:
filename = os.path.join(outputFolder, fi)
f=open(filename, 'r')
dataDict = pickle.load(f)
f.close()
except:
sys.stderr.write('Folder {} does not contain densities trajectories file.'.format(outputFolder))
sys.exit()
else:
if not options.simulated:
d,c, movie_length = dataDict['tracklets dictionary'], dataDict['connexions between tracklets'], dataDict['movie_length']
res = histogramPreparationFromTracklets(d, c, outputFolder, False, verbose, movie_length, name=fi_trajfeatures,
filtering_fusion=settings.filtering_fusion)
else:
raise AttributeError
# d=ensTraj()
# for traj in dataDict:
# t = trajectoire(1, xi=None, yi=None, frame=None, idC=None, id=1)
# t.lstPoints = traj
# d.lstTraj.append(t)
# res=histogramPreparationFromTracklets({options.plate : {options.well : d}}, None,
# outputFolder,training =True, verbose=verbose, movie_length={options.plate : {options.well :99}},
# name=fi_trajfeatures) #(d,c, outputFolder, False, verbose, tab=True, length=movie_length)
def extractFeatures(self, traj_filename,feat_filename, verbose):
fp = open(os.path.join(self.settings.out_folder, "{}.pkl".format(traj_filename)))
data=pickle.load(fp); fp.close()
d,c, movie_length = data['tracklets dictionary'], data['connexions between tracklets'], data['movie_length']
res = histogramPreparationFromTracklets(d, c, self.settings.out_folder, True, verbose, movie_length, name=feat_filename)