--- a/scripts/process.py	Tue Feb 27 16:46:06 2018 -0500
+++ b/scripts/process.py	Tue Mar 06 08:26:13 2018 -0500
@@ -1,17 +1,48 @@
 #! /usr/bin/env python
 
 import sys, argparse
+from pathlib2 import Path
 
-import storage, cvutils, utils
+import storage, events, prediction
 from metadata import *
 
 parser = argparse.ArgumentParser(description='This program manages the processing of several files based on a description of the sites and video data in an SQLite database following the metadata module.')
 parser.add_argument('--db', dest = 'metadataFilename', help = 'name of the metadata file', required = True)
+parser.add_argument('--videos', dest = 'videoIds', help = 'indices of the video sequences', nargs = '*', type = int)
+parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
 
 # need way of selecting sites as similar as possible to sql alchemy syntax
 # override tracking.cfg from db
 # manage cfg files, overwrite them (or a subset of parameters)
 # delete sqlite files
-# nprocesses
+
+parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
 
 args = parser.parse_args()
+# files are relative to metadata location
+
+session = createDatabase(args.metadataFilename)
+parentDir = Path(args.metadataFilename).parent
+
+# todo change prediction parameters
+predictionParameters = prediction.CVExactPredictionParameters()
+
+for videoId in args.videoIds:
+    vs = session.query(VideoSequence).get(videoId)
+    print(vs.getDatabaseFilename())
+    objects = storage.loadTrajectoriesFromSqlite(str(parentDir/vs.getDatabaseFilename()), 'object')#, args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
+    interactions = events.createInteractions(objects)
+    #if args.nProcesses == 1:
+    params = storage.ProcessParameters(str(parentDir/vs.cameraView.getTrackingConfigurationFilename()))
+    #print(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
+    processed = events.computeIndicators(interactions, True, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
+    storage.saveIndicatorsToSqlite(str(parentDir/vs.getDatabaseFilename()), processed)
+    
+# else:
+#     pool = Pool(processes = args.nProcesses)
+#     nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
+#     jobs = [pool.apply_async(events.computeIndicators, args = (interactions[i*nInteractionPerProcess:(i+1)*nInteractionPerProcess], not args.noMotionPrediction, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)) for i in range(args.nProcesses)]
+#     processed = []
+#     for job in jobs:
+#         processed += job.get()
+#     pool.close()