Mercurial Hosting > traffic-intelligence
view python/compute-object-from-features.py @ 105:9844c69d8fa2
added multiply method to Point
author | Nicolas Saunier <nico@confins.net> |
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date | Thu, 14 Jul 2011 19:48:30 -0400 |
parents | 1621b46a1523 |
children | ce4cb46b3603 |
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#!/usr/bin/env python import sys import matplotlib.mlab as pylab import matplotlib.pyplot as plt import numpy as np import cv import utils import cvutils import ubc_utils import moving # use something like getopt to manage arguments if necessary if len(sys.argv) < 3: print('Usage: {0} <video-filename> <n-objects>'.format(sys.argv[0])) sys.exit() if sys.argv[1].endswith('.avi'): videoFilenamePrefix = utils.removeExtension(sys.argv[1],'.') else: videoFilenamePrefix = sys.argv[1] objectNum = int(sys.argv[2]) objects = ubc_utils.loadTrajectories(videoFilenamePrefix+'-objects.txt', objectNum+1) obj = objects[objectNum] features = ubc_utils.loadTrajectories(videoFilenamePrefix+'-features.txt', max(obj.featureNumbers)+1) h = np.loadtxt(videoFilenamePrefix+'-homography.txt') invh = cvutils.invertHomography(h) def computeObject(features, homography, timeInterval = None): if not timeInterval: raise Exception('not implemented') # compute from the features yCoordinates = -np.ones((len(features),int(timeInterval.length()))) for i,f in enumerate(features): traj = f.getPositions().asArray() imgTraj = cvutils.projectArray(homography, traj) yCoordinates[i,f.getFirstInstant()-timeInterval.first:f.getLastInstant()+1-timeInterval.first] = imgTraj[1,:] indices = np.argmax(yCoordinates,0) newTraj = moving.Trajectory() for j,idx in enumerate(indices): newTraj.addPosition(features[idx].getPositionAtInstant(j+timeInterval.first)) #newVelocities.addPosition(features[obj.featureNumbers[idx]].getVelocityAtInstant(j+obj.getFirstInstant())) return newTraj # TODO version median def kalmanFilter(positions, velocities, processNoiseCov, measurementNoiseCov): kalman=cv.CreateKalman(6, 4) kalman.transition_matrix[0,2]=1 kalman.transition_matrix[0,4]=1./2 kalman.transition_matrix[1,3]=1 kalman.transition_matrix[1,5]=1./2 kalman.transition_matrix[2,4]=1 kalman.transition_matrix[3,5]=1 cv.SetIdentity(kalman.measurement_matrix, 1.) cv.SetIdentity(kalman.process_noise_cov, processNoiseCov) cv.SetIdentity(kalman.measurement_noise_cov, measurementNoiseCov) cv.SetIdentity(kalman.error_cov_post, 1.) p = positions[0] v = velocities[0] v2 = velocities[2] a = (v2-v).multiply(0.5) kalman.state_post[0,0]=p.x kalman.state_post[1,0]=p.y kalman.state_post[2,0]=v.x kalman.state_post[3,0]=v.y kalman.state_post[4,0]=a.x kalman.state_post[5,0]=a.y filteredPositions = moving.Trajectory() filteredVelocities = moving.Trajectory() measurement = cv.CreateMat(4,1,cv.CV_32FC1) for i in xrange(positions.length()): cv.KalmanPredict(kalman) # no control p = positions[i] v = velocities[i] measurement[0,0] = p.x measurement[1,0] = p.y measurement[2,0] = v.x measurement[3,0] = v.y cv.KalmanCorrect(kalman, measurement) filteredPositions.addPositionXY(kalman.state_post[0,0], kalman.state_post[1,0]) filteredVelocities.addPositionXY(kalman.state_post[2,0], kalman.state_post[3,0]) return (filteredPositions, filteredVelocities) (filteredPositions, filteredVelocities) = kalmanFilter(newTraj, obj.getVelocities(), 1e-5, 1e-1) plt.clf() obj.draw('rx') for fnum in obj.featureNumbers: features[fnum].draw() newTraj.draw('bx') plt.axis('equal')