def render(filename, color, theta, dest): skel = Skeleton(stereo_cam) skel.load(filename, load_PR = False) skel.optimize() skel.optimize() skel.plot(color, False, theta) xlim = pylab.xlim() ylim = pylab.ylim() xrange = xlim[1] - xlim[0] yrange = ylim[1] - ylim[0] r = max(xrange, yrange) * 0.5 # 0.32 mid = sum(xlim) / 2 pylab.xlim(mid - r, mid + r) mid = sum(ylim) / 2 pylab.ylim(mid - r, mid + r)
pf.close() if 1: del nf.rawdata del nf.lgrad del nf.rgrad del nf.lf del nf.rf del L del R nf.lf = None f.append(nf) if skel: skel.optimize() skel.plot('blue', True) #pylab.plot([x for (x,y,z) in gt], [z for (x,y,z) in gt], c = 'g') pylab.show() node_ids = [f.id for f in skel.nodes] for i in sorted(node_ids): f = open("trial/%06d.pose.pickle" % i, "w") pickle.dump(skel.newpose(i), f) f.close() sys.exit(1) gtc = [p.xform(0, 0, 0) for p in gt] # Write confusion matrix to file - for Patrick if 0: confusion = [[vo.proximity(a, b, True)[0] for a in f] for b in f] pylab.pcolor(numpy.array(confusion))
pylab.plot(oe_x, oe_y, c = 'green', label = ground_truth_label) pts = dict([ (f,skel.newpose(f.id).xform(0,0,0)) for f in skel.nodes ]) nodepts = pts.values() pval = planar(numpy.array([x for (x,y,z) in nodepts]), numpy.array([y for (x,y,z) in nodepts]), numpy.array([z for (x,y,z) in nodepts])) print "planarity of skeleton: ", pval skel.optimize() pts = dict([ (f,skel.newpose(f).xform(0,0,0)) for f in skel.nodes ]) nodepts = pts.values() pval = planar(numpy.array([x for (x,y,z) in nodepts]), numpy.array([y for (x,y,z) in nodepts]), numpy.array([z for (x,y,z) in nodepts])) print "planarity of skeleton: ", pval skel.plot('blue') print vos[0].log_keyframes xlim = pylab.xlim() ylim = pylab.ylim() xrange = xlim[1] - xlim[0] yrange = ylim[1] - ylim[0] r = max(xrange, yrange) * 0.75 mid = sum(xlim) / 2 pylab.xlim(mid - r, mid + r) mid = sum(ylim) / 2 pylab.ylim(mid - r, mid + r) pylab.legend() pylab.savefig("foo.png", dpi=200)