Mercurial Hosting > traffic-intelligence
view scripts/learn-poi.py @ 912:fd057a6b04db
corrected bug
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 28 Jun 2017 16:51:17 -0400 |
parents | 6db83beb5350 |
children | 1cd878812529 |
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#! /usr/bin/env python import argparse import numpy as np from sklearn import mixture import matplotlib.pyplot as plt import storage, ml 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). TODO: save the data') parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True) parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['feature', 'object'], default = 'object') parser.add_argument('-norigins', dest = 'nOriginClusters', help = 'number of clusters for trajectory origins', required = True, type = int) parser.add_argument('-ndestinations', dest = 'nDestinationClusters', help = 'number of clusters for trajectory destinations (=norigins if not provided)', type = int) parser.add_argument('--covariance-type', dest = 'covarianceType', help = 'type of covariance of Gaussian model', default = "full") parser.add_argument('-w', dest = 'worldImageFilename', help = 'filename of the world image') parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units of distance per pixel', type = float, default = 1.) parser.add_argument('--display', dest = 'display', help = 'display points of interests', action = 'store_true') # default is manhattan distance args = parser.parse_args() objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, args.trajectoryType) beginnings = [] ends = [] for o in objects: beginnings.append(o.getPositionAt(0).aslist()) ends.append(o.getPositionAt(int(o.length())-1).aslist()) beginnings = np.array(beginnings) ends = np.array(ends) nDestinationClusters = args.nDestinationClusters if args.nDestinationClusters is None: nDestinationClusters = args.nOriginClusters gmmId=0 for nClusters, points, gmmType in zip([args.nOriginClusters, nDestinationClusters], [beginnings, ends], ['beginning', 'end']): # estimation gmm = mixture.GaussianMixture(n_components=nClusters, covariance_type = args.covarianceType) model=gmm.fit(beginnings) if not model.converged_: print('Warning: model for '+gmmType+' points did not converge') # plot if args.display: fig = plt.figure() if args.worldImageFilename is not None and args.unitsPerPixel is not None: img = plt.imread(args.worldImageFilename) plt.imshow(img) labels = ml.plotGMMClusters(model, points, fig, nUnitsPerPixel = args.unitsPerPixel) plt.axis('image') plt.title(gmmType) print(gmmType+' Clusters:\n{}'.format(ml.computeClusterSizes(labels, range(model.n_components)))) # save storage.savePOIs(args.databaseFilename, model, gmmType, gmmId) gmmId += 1 if args.display: plt.axis('equal') plt.show() # fig = plt.figure() # if args.worldImageFilename is not None and args.pixelsPerUnit is not None: # img = plt.imread(args.worldImageFilename) # plt.imshow(img) # ml.plotGMMClusters(, , fig, nPixelsPerUnit = args.pixelsPerUnit) # plt.axis('equal') # plt.title() # print('Destination Clusters:\n{}'.format(ml.computeClusterSizes(endModel.predict(ends), range(args.nClusters))))