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)
