#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