コード例 #1
0
    def cc(self,t):

        # perform background subtraction
        (dfore,bw) = self.bg.sub_bg(t+params.start_frame)
        
        # for each pixel, find the target it most likely belongs to
        (y,x) = num.where(bw)
        mind = num.zeros(y.shape)
        mind[:] = num.inf
        closest = num.zeros(y.shape)
        for targ in self.tracks[t].itervalues():
            S = est.ell2cov(targ.major,targ.minor,targ.angle)
            Sinv = num.linalg.inv(S)
            xx = x.astype(float) - targ.x
            yy = y.astype(float) - targ.y
            d = xx**2*Sinv[0,0] + 2*Sinv[0,1]*xx*yy + yy**2*Sinv[1,1]
            issmall = d <= mind
            mind[issmall] = d[issmall]
            closest[issmall] = targ.identity
        
        # set each pixel in L to belong to the closest target
        L = num.zeros(bw.shape)
        L[bw] = closest+1

        #mpl.imshow(L)
        #mpl.show()

        return (L,dfore)
コード例 #2
0
def splitobservation(bw,dfore,k,init):

    (r,c) = num.where(bw)

    if DEBUG: print 'number of pixels in component being split: %d'%len(r)
    x = num.hstack((c.reshape(c.size,1),r.reshape(r.size,1)))
    w = dfore[bw]
    if DEBUG: print 'data being clustered: '
    if DEBUG: print x
    if DEBUG: print 'with weights: '
    if DEBUG: print w

    # create means and covariance matrices to initialize
    mu0 = num.zeros((k,2))
    S0 = num.zeros((k,2,2))
    priors0 = num.zeros(k)
    for i in range(k):
        if DEBUG: print 'predicted ellipse %d: '%i + str(init[i])
        mu0[i,0] = init[i].x
        mu0[i,1] = init[i].y
        S0[:,:,i] = est.ell2cov(init[i].major,init[i].minor,init[i].angle)
        priors0[i] = init[i].area
        (tmpmajor,tmpminor,tmpangle) = est.cov2ell(S0[:,:,i])
    priors0 = priors0 / num.sum(priors0)
    if DEBUG: print 'initializing with '
    if DEBUG: print 'mu0 = '
    if DEBUG: print mu0
    if DEBUG: print 'S0 = '
    if DEBUG:
        for i in range(k):
            print S0[:,:,i]
    if DEBUG: print 'priors0 = '
    if DEBUG: print priors0
 
   # are there no data points?
    if len(r) == 0:
        return None
        
    (mu,S,priors,gamma,negloglik) = kcluster.gmmem(x,mu0,S0,priors0,weights=w,thresh=.1,mincov=.015625)
            
    obs = []
    for i in range(k):
	(major,minor,angle) = est.cov2ell(S[:,:,i])
	obs.append(ell.Ellipse(mu[i,0],mu[i,1],minor,major,angle))
	obs[-1].compute_area()

    return obs