tsImages = getRecord(AFW(minw=minw, minh=minh), 10000)
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 != "":
minw = args.minw
minh = args.minh
nit = args.nit
detfile = args.detfile
if detfile == "":
raise ValueError('Please determine the detection file!')
minpix = int(np.sqrt(0.5 * minw * minh))
# Determine whether we are dealing with fddb or not
if detfile.lower().find('pascal') != -1:
tsImages = getRecord(
PASCALfaces(minw=minw, minh=minh, useOldAnn=args.oldAnn), 10000)
ovr = 0.5
is_point = False
dets = loadDetections(detfile)
dets = filterdet(dets, minpix)
color, label = getColorLabel(detfile)
r = evaluate_optim(tsImages,
dets,
label,
color,
point=is_point,
iter=nit,
ovr=ovr)
print('{}'.format(r[0]))
elif detfile.lower().find('afw') != -1:
tsImages = getRecord(AFW(minw=minw, minh=minh, useOldAnn=args.oldAnn),
10000)
ovr = 0.5
pylab.rcParams.update(params)
###
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)
# BASELINES
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)
res.append(r)
res.sort(key=lambda tup: tup[0], reverse=True)
ii = []