def check_results(aligned, vs_ref=False):
assert "pgm" in argv[3] # only for pgms
sse = 0.0
for i in xrange(len(aligned)):
bn = splitext(basename(argv[3 + i]))[0].lower()
if vs_ref: # Michael comparison
pts = loadtxt("data/bioid/ref_impl_facepts/%s.facepts" % bn)
else: # ground truth comparison
pts = read_pointsfile("data/bioid/points/%s.pts" % bn)
# visualize(argv[3+i],pts)
err = ((aligned[i] - pts) ** 2).sum()
print bn, err
sse += err
print "total error:", sse
print "avg per img:", sse / float(len(aligned))
def check_results(aligned, vs_ref=False):
assert 'pgm' in argv[3] # only for pgms
sse = 0.0
for i in xrange(len(aligned)):
bn = splitext(basename(argv[3 + i]))[0].lower()
if vs_ref: # Michael comparison
pts = loadtxt("data/bioid/ref_impl_facepts/%s.facepts" % bn)
else: # ground truth comparison
pts = read_pointsfile("data/bioid/points/%s.pts" % bn)
#visualize(argv[3+i],pts)
err = ((aligned[i] - pts)**2).sum()
print bn, err
sse += err
print "total error:", sse
print "avg per img:", sse / float(len(aligned))
def main(img_dir,pts_dir,haar_file,num_trains):
t = Trainer()
cascade = load_cascade(haar_file)
sglob = lambda dir: sorted(glob(dir+"/*"))
paths = izip(sglob(img_dir),sglob(pts_dir))
# split into training and testing
for im,pf in islice(paths,num_trains):
img = pvImage(im)
pts = read_pointsfile(pf)
faces = detect_faces(img,cascade)
if faces:
t.add_training_image(img,pts,faces[0])
else:
print "No face found for %s"%im
t.train()
t.export_aligner('aligner.yaml')
