pylab.figure(figsize=(10, 7)) res = [] for fn in glob.glob(os.path.join(baseFolder, "*")): print fn ovr = 0.5 is_point = False ff = os.path.basename(fn).split(".") if ff[0] == "Face++" or ff[0] == "Picasa" or ff[0] == "Face": is_point = True if ff[0] == "Picasa": # and ff[1]=="cvs": # special case for picasa # we evaluated manually, selecting overlap threshold of 0.1 gives # in this case the correct result ovr = 0.1 dets = loadDetections(fn) dets = filterdet(dets, minpix) color, label = getColorLabel(fn) r = evaluate_optim( tsImages, dets, label, color, point=is_point, iter=nit, ovr=ovr) res.append(r) # <codecell> # <codecell> # current plot if detfile != "": dets = loadDetections(detfile)
### pylab.figure(figsize=(10, 7)) res = [] for fn in glob.glob(os.path.join(baseFolder, "*")): ovr = 0.5 is_point = False ff = os.path.basename(fn).split(".") if ff[0] == "Face++" or ff[0] == "Picasa" or ff[0] == "Face": is_point = True if ff[0] == "Picasa": # and ff[1]=="cvs": # special case for picasa # we evaluated manually, selecting overlap threshold of 0.1 gives # in this case the correct result ovr = 0.1 dets = loadDetections(fn) dets = filterdet(dets, minpix) color, label = getColorLabel(fn) r = evaluate_optim(tsImages, dets, label, color, point=is_point, iter=nit, ovr=ovr) res.append(r) # current plot if args.detfile != "": dets = loadDetections(args.detfile) dets = filterdet(dets, minpix) r = evaluate_optim(tsImages, dets, args.detfile, 'green', iter=nit)