def visualize(fname,points,index=None,face_rect=None):
pvimg = pvImage(fname)
for p in points: pvimg.annotatePoint(p)
if index: pvimg.annotateLabel((0,0),str(index))
if face_rect: pvimg.annotateRect(face_rect)
name,ext = splitext(basename(fname))
pvimg.asAnnotated().save("%s_viz%s%s"%(name,'_%02d'%index if index else '',ext))
def main(aligner_fname, cascade_fname, image_fnames):
aligner = Aligner(aligner_fname)
cascade = load_cascade(cascade_fname)
images = (pvImage(fname) for fname in image_fnames)
return [
aligner.align_face(detect_faces(img, cascade)[0], img)
for img in images
]
def visualize(fname, points, index=None, face_rect=None):
pvimg = pvImage(fname)
for p in points:
pvimg.annotatePoint(p)
if index: pvimg.annotateLabel((0, 0), str(index))
if face_rect: pvimg.annotateRect(face_rect)
name, ext = splitext(basename(fname))
pvimg.asAnnotated().save("%s_viz%s%s" %
(name, '_%02d' % index if index else '', ext))
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')
def main(aligner_fname, cascade_fname, image_fnames):
aligner = Aligner(aligner_fname)
cascade = load_cascade(cascade_fname)
images = (pvImage(fname) for fname in image_fnames)
return [aligner.align_face(detect_faces(img, cascade)[0], img) for img in images]
