#! /usr/bin/env python3
import sys, argparse
import numpy as np
import matplotlib.pyplot as plt
from trafficintelligence import ml, utils, storage, moving, processing
parser = argparse.ArgumentParser(description='''The program clusters trajectories, each cluster being represented by a trajectory. It can either work on the same dataset (database) or different ones, but only does learning or assignment at a time to avoid issues''') #, epilog = ''
#parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
parser.add_argument('-o', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes')
parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to process', choices = ['feature', 'object'], default = 'feature')
parser.add_argument('--nfeatures-per-object', dest = 'nLongestFeaturesPerObject', help = 'maximum number of features per object to load', type = int)
parser.add_argument('-n', dest = 'nObjects', help = 'number of the object or feature trajectories to load', type = int, default = None)
parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
#parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0)
parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true')
parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
parser.add_argument('--similarities-filename', dest = 'similaritiesFilename', help = 'filename of the similarities')
parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
parser.add_argument('--save-assignments', dest = 'saveAssignments', help = 'saves the assignments of the objects to the prototypes', action = 'store_true')
parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true')
args = parser.parse_args()
# use cases
# 1. learn proto from one file, save in same or another
# 2. load proto, load objects (from same or other db), update proto matchings, save proto
# TODO 3. on same dataset, learn and assign trajectories (could be done with min cluster size)
# TODO? 4. when assigning, allow min cluster size only to avoid assigning to small clusters (but prototypes are not removed even if in small clusters, can be done after assignment with nmatchings)
# TODO add possibility to cluster with velocities
# TODO add possibility to load all trajectories and use minclustersize
if args.learn and args.assign:
print('Cannot learn and assign simultaneously')
sys.exit(0)
objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nObjects, timeStep = args.positionSubsamplingRate, nLongestFeaturesPerObject = args.nLongestFeaturesPerObject)
if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None:
objectsWithFeatures = objects
objects = [f for o in objectsWithFeatures for f in o.getFeatures()]
prototypeType = 'feature'
else:
prototypeType = args.trajectoryType
# load initial prototypes, if any
if args.inputPrototypeDatabaseFilename is not None:
initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
else:
initialPrototypes = []
lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
nTrajectories = len(initialPrototypes)+len(objects)
if args.similaritiesFilename is not None:
similarities = np.loadtxt(args.similaritiesFilename)
if args.similaritiesFilename is None or similarities.shape[0] != nTrajectories or similarities.shape[1] != nTrajectories:
similarities = -np.ones((nTrajectories, nTrajectories))
prototypeIndices, labels = processing.learnAssignMotionPatterns(args.learn, args.assign, objects, similarities, args.minSimilarity, similarityFunc, 0, args.optimizeCentroid, args.randomInitialization, False, initialPrototypes)
if args.learn:# and not args.assign:
prototypes = []
for i in prototypeIndices:
if i<len(initialPrototypes):
prototypes.append(initialPrototypes[i])
else:
prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), prototypeType))
if args.outputPrototypeDatabaseFilename is None:
outputPrototypeDatabaseFilename = args.databaseFilename
else:
outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename:
storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype')
storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)
if args.display:
plt.figure()
for p in prototypes:
p.getMovingObject().plot()
plt.axis('equal')
plt.show()
if args.assign: # not args.learn and no modification to prototypes, can work with initialPrototypes
clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
for i in prototypeIndices:
nMatchings = clusterSizes[i]-1 # external prototypes
if initialPrototypes[i].nMatchings is None:
initialPrototypes[i].nMatchings = nMatchings
else:
initialPrototypes[i].nMatchings += nMatchings
if args.outputPrototypeDatabaseFilename is None:
outputPrototypeDatabaseFilename = args.databaseFilename
else:
outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
storage.setPrototypeMatchingsInSqlite(outputPrototypeDatabaseFilename, initialPrototypes)
if args.saveAssignments:
if args.trajectoryType == 'object' and args.nLongestFeaturesPerObject is not None:
# consider that the object is assigned through its longest features
# issues are inconsistencies in the number of matchings per prototype and display (will display features, not objects)
objectNumbers = []
objectLabels = []
i = 0
for obj in objectsWithFeatures:
objLabels = []
for f in obj.getFeatures():
if f == objects[i]:
objLabels.append(labels[i+len(initialPrototypes)])
i += 1
else:
print('Issue with obj {} and feature {} (trajectory {})'.format(obj.getNum(), f.getNum(), i))
objectLabels.append(utils.mostCommon(objLabels))
objectNumbers.append(obj.getNum())
storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objectNumbers, 'object', objectLabels, initialPrototypes)
else:
storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, [obj.getNum() for obj in objects], args.trajectoryType, labels[len(initialPrototypes):], initialPrototypes)
if args.display:
plt.figure()
for i,o in enumerate(objects):
if labels[i+len(initialPrototypes)] < 0:
o.plot('kx-')
else:
o.plot(utils.colors[labels[i+len(initialPrototypes)]])
for i,p in enumerate(initialPrototypes):
p.getMovingObject().plot(utils.colors[i]+'o')
plt.axis('equal')
plt.show()
if (args.learn or args.assign) and args.saveSimilarities:
if args.similaritiesFilename is not None:
np.savetxt(args.similaritiesFilename, similarities, '%.4f')
else:
np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')
