Mercurial Hosting > traffic-intelligence
comparison scripts/learn-motion-patterns.py @ 734:1d4dcb5c8708 dev
first example script to learn prototypes
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
---|---|
date | Tue, 11 Aug 2015 12:55:09 -0400 |
parents | |
children | 0e875a7f5759 |
comparison
equal
deleted
inserted
replaced
733:c35e4a4b199d | 734:1d4dcb5c8708 |
---|---|
1 #! /usr/bin/env python | |
2 | |
3 import sys, argparse | |
4 | |
5 #import matplotlib.pyplot as plt | |
6 import numpy as np | |
7 | |
8 import ml, utils, storage | |
9 | |
10 parser = argparse.ArgumentParser(description='The program learns prototypes for the motion patterns') #, epilog = '' | |
11 #parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file') | |
12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True) | |
13 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures') | |
14 parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None) | |
15 parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True) | |
16 parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance | |
17 parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True) | |
18 parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = None) | |
19 parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true') # default is manhattan distance | |
20 | |
21 args = parser.parse_args() | |
22 | |
23 # TODO parameters (random init?) and what to learn from: objects, features, longest features from objects | |
24 # TODO add possibility to cluter with velocities | |
25 | |
26 trajectoryType = args.trajectoryType | |
27 if args.trajectoryType == 'objectfeatures': | |
28 trajectoryType = 'object' | |
29 | |
30 #features = storage.loadTrajectoriesFromSqlite(databaseFilename, args.trajectoryType) | |
31 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, withFeatures = (args.trajectoryType == 'objectfeatures'), objectNumbers = args.nTrajectories) | |
32 | |
33 if args.trajectoryType == 'objectfeatures': | |
34 features = [] | |
35 for o in objects: | |
36 tmp = utils.sortByLength(o.getFeatures(), reverse = True) | |
37 features += tmp[:min(len(tmp), 3)] | |
38 objects = features | |
39 | |
40 trajectories = [o.getPositions().asArray().T for o in objects] | |
41 | |
42 lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) | |
43 nTrajectories = len(trajectories) | |
44 | |
45 similarities = np.zeros((nTrajectories, nTrajectories)) | |
46 for i in xrange(nTrajectories): | |
47 for j in xrange(i): | |
48 similarities[i,j] = lcss.computeNormalized(trajectories[i], trajectories[j]) | |
49 similarities[j,i] = similarities[i,j] | |
50 | |
51 prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, args.minClusterSize) | |
52 | |
53 if args.display: | |
54 for i,o in enumerate(objects): | |
55 if i not in prototypeIndices: | |
56 o.plot(utils.colors[labels[i]]) | |
57 for i in prototypeIndices: | |
58 objects[i].plot(utils.colors[i]+'o') | |
59 | |
60 # TODO store the prototypes (if features, easy, if objects, info must be stored about the type) |