#load the model
m=util2.load("%s%d.model"%(modelname,it))
import pylab
#show the model
if True:
print "Show model"
pylab.figure(100)
pylab.clf()
util2.drawModel(m["ww"])
pylab.draw()
print "---- Image %s----"%imname
print
img=util2.myimread(imname)
#compute the HOG pyramid
f=pyrHOG2.pyrHOG(img,interv=10,savedir="",notload=True,notsave=True,hallucinate=True,cformat=True)
print
print "Complete search"
showImage(img,title="Complete search")
res=pyrHOG2.detect(f,m,bottomup=True,deform=True,usemrf=True,small=False,show=True)
pylab.axis((0,img.shape[1],img.shape[0],0))
dettime1=res[2]
numhog1=res[3]
print "Number of computed HOGs:",numhog1
print
print "Coarse-to-Fine search"
import pylab
showImage(img,title="Coarse-to-Fine")
res=pyrHOG2.detect(f,m,bottomup=False,deform=True,usemrf=True,small=False,show=True)
#configuration class
class config(object):
pass
cfg=config()
cfg.testname="./data/finalRL/%s2_test" #object model
cfg.resize=1.0 #resize the input image
cfg.hallucinate=True #use HOGs up to 4 pixels
cfg.initr=1 #initial radious of the CtF search
cfg.ratio=1 #radious at the next levels
cfg.deform=True #use deformation
cfg.bottomup=False #use complete search
cfg.usemrf=True #use lateral constraints
#read the image
img=util2.myimread(imname,resize=cfg.resize)
#compute the hog pyramid
f=pyrHOG2.pyrHOG(img,interv=10,savedir="",notsave=True,notload=True,hallucinate=cfg.hallucinate,cformat=True)
#show the image
fig=pylab.figure(20)
pylab.ioff()
axes=pylab.Axes(fig, [.0,.0,1.0,1.0])
fig.add_axes(axes)
pylab.imshow(img,interpolation="nearest",animated=True)
t=time.time()
#for each class
for ccls in cls:
print
print "Class: %s"%ccls
#load the class model
