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annotate scripts/learn-poi.py @ 1240:bb14f919d1cb

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cleaned use of centile (np only) and added info in classify-objects
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 05 Feb 2024 14:14:14 -0500
parents cc5cb04b04b0
children
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rev   line source
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1 #! /usr/bin/env python3
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2
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3 import argparse
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4
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5 import numpy as np
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6 from sklearn import mixture
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7 import matplotlib.pyplot as plt
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8
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9 from trafficintelligence import storage, ml
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10
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11 parser = argparse.ArgumentParser(description='The program learns and displays Gaussians fit to beginnings and ends of object trajectories (based on Mohamed Gomaa Mohamed 2015 PhD).')
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12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
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13 parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['feature', 'object'], default = 'object')
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14 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to display', type = int)
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15 parser.add_argument('-norigins', dest = 'nOriginClusters', help = 'number of clusters for trajectory origins', required = True, type = int)
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16 parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int)
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17 parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full")
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18 parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image')
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19 parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.)
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20 parser.add_argument('--display', dest = 'display', help = 'displays points of interests', action = 'store_true') # default is manhattan distance
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21 parser.add_argument('--assign', dest = 'assign', help = 'assigns the trajectories to the POIs and saves the assignments', action = 'store_true')
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22 parser.add_argument('--display-paths', dest = 'displayPaths', help = 'displays all possible origin destination if assignment is done', action = 'store_true')
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23
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24 # TODO test Variational Bayesian Gaussian Mixture BayesianGaussianMixture
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25
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26 args = parser.parse_args()
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27
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28 objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType, args.nObjects)
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29
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30 beginnings = []
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31 ends = []
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32 for o in objects:
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33 beginnings.append(o.getPositionAt(0).aslist())
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34 ends.append(o.getPositionAt(int(o.length())-1).aslist())
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35 if args.assign:
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36 o.od = [-1, -1]
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37
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38 beginnings = np.array(beginnings)
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39 ends = np.array(ends)
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40
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41 nDestinationClusters = args.nDestinationClusters
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42 if args.nDestinationClusters is None:
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43 nDestinationClusters = args.nOriginClusters
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44
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45 gmmId=0
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46 models = {}
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47 for nClusters, points, gmmType in zip([args.nOriginClusters, nDestinationClusters],
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48 [beginnings, ends],
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49 ['beginning', 'end']):
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50 # estimation
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51 gmm = mixture.GaussianMixture(n_components=nClusters, covariance_type = args.covarianceType)
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52 models[gmmType]=gmm.fit(points)
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53 if not models[gmmType].converged_:
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54 print('Warning: model for '+gmmType+' points did not converge')
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55 if args.display or args.assign:
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56 labels = models[gmmType].predict(points)
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57 # plot
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58 if args.display:
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59 fig = plt.figure()
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60 if args.worldImageFilename is not None and args.unitsPerPixel is not None:
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61 img = plt.imread(args.worldImageFilename)
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62 plt.imshow(img)
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63 ml.plotGMMClusters(models[gmmType], labels, points, fig, nUnitsPerPixel = args.unitsPerPixel)
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64 plt.axis('image')
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65 plt.title(gmmType)
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66 print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(models[gmmType].n_components))))
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67 # save
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68 storage.savePOIsToSqlite(args.databaseFilename, models[gmmType], gmmType, gmmId)
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69 # save assignments
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70 if args.assign:
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71 for o, l in zip(objects, labels):
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72 if gmmType == 'beginning':
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73 o.od[0] = l
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74 elif gmmType == 'end':
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75 o.od[1] = l
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76 gmmId += 1
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77
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78 if args.assign:
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79 storage.savePOIAssignments(args.databaseFilename, objects)
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80 if args.displayPaths:
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81 for i in range(args.nOriginClusters):
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82 for j in range(args.nDestinationClusters):
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83 odObjects = [o for o in objects if o.od[0] == i and o.od[1] == j]
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84 if len(odObjects) > 0:
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85 fig = plt.figure()
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86 ax = fig.add_subplot(111)
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87 ml.plotGMM(models['beginning'].means_[i], models['beginning'].covariances_[i], i, fig, 'b')
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88 ml.plotGMM(models['end'].means_[j], models['end'].covariances_[j], j, fig, 'r')
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89 for o in odObjects:
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90 o.plot(withOrigin = True)
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91 plt.title('OD {} to {}'.format(i,j))
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92 plt.axis('equal')
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93 plt.show()
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94
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96 if args.display:
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97 plt.axis('equal')
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98 plt.show()